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  • 51.
    Fresk, Emil
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Wuthier, David
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Generalized center of gravity compensation for multirotors with application to aerial manipulation2017In: IEEE International Conference on Intelligent Robots and Systems, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 4424-4429, article id 8206307Conference paper (Refereed)
    Abstract [en]

    The aim of this paper is to establish a generalized parameter estimation scheme to online estimate the Center of Gravity (COG) for multirotors, while using a geometric controller to perform position tracking for applications in aerial manipulation. The proposed scheme is developed so the controller uses the estimated COG to compensate and remove constant offset in the position tracking. The efficiency and validity of the proposed parameter estimation and compensation scheme is proved through two experimental evaluations, one when step changes to the COG are applied and one tracking experiment where a compact aerial manipulator is attached to the multirotor and performs sweeping motions.

    The full text will be freely available from 2019-12-14 09:08
  • 52.
    Fresk, Emil
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Ödmark, Kristoffer
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Ultra WideBand enabled Inertial Odometry for Generic Localization2017In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 11465-11472Article in journal (Refereed)
    Abstract [en]

    In this paper we will present a inertial odometry localization system, utilizing Ultra WideBand distance measurements for corrections, as a generic localization solution. The proposed scheme is evaluated in two different measurement schemes, one cyclic and one based on stochastic events, which has the strong merit of minimizing the sampling rate, while adhering to covariance constraints on the state, allowing the system to conform with RF regulations. The efficacy of the proposed scheme is evaluated in extended experimental evaluation on an hexacopter Unmanned Aerial Vehicle

  • 53.
    Gavrilis, Dimitris
    et al.
    Department of Electrical Engineering and Computer Technology, University of Patras.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vasiloglou, Nikolaos
    Ismion Inc.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Inelligent Assistant for Physicians2016In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL, 17-20 August 2016,, 2016Conference paper (Refereed)
    Abstract [en]

    This paper presents a software tool developed for assisting physicians during an examination process. The tool consists of a number of modules with the aim to make the examination process not only quicker but also fault proof moving from a simple electronic medical records management system towards an intelligent assistant for the physician. The intelligent component exploits users inputs as well as well established standards to line up possible suggestions for filling in the examination report. As the physician continues using it, the tool keeps extracting new knowledge. The architecture of the tool is presented in brief while the intelligent component which builds upon the notion of multilabel learning is presented in more detail. Our preliminary results from a real test case indicate that the performance of the intelligent module can reach quite high performance without a large amount of data.

  • 54.
    Gavrilis, Dimitris
    et al.
    Department of Electrical Engineering and Computer Technology, University of Patras.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vasiloglou, Nikolaos
    Ismion Inc.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Intelligent Assistant for Physicians2016In: IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC): Orlando, FL, USA, 16-20 Aug. 2016, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 2586-2589, article id 7591259Conference paper (Refereed)
    Abstract [en]

    This paper presents a software tool developed for assisting physicians during an examination process. The tool consists of a number of modules with the aim to make the examination process not only quicker but also fault proof moving from a simple electronic medical records management system towards an intelligent assistant for the physician. The intelligent component exploits users inputs as well as well established standards to line up possible suggestions for filling in the examination report. As the physician continues using it, the tool keeps extracting new knowledge. The architecture of the tool is presented in brief while the intelligent component which builds upon the notion of multilabel learning is presented in more detail. Our preliminary results from a real test case indicate that the performance of the intelligent module can reach quite high performance without a large amount of data.

  • 55.
    Gavrilis, Dimitris
    et al.
    Digital Curation Unit - IMIS, Athena Research Center, Maroussi.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi, Department of Computer Engineering, TEI of Epirus, Arta.
    A One-Class Approach to Cardiotocogram Assessment2015In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: EMBC 2015, Milan; Italy, 25-29 August 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 518-521, article id 7318413Conference paper (Refereed)
    Abstract [en]

    Cardiotocogram (CTG) is the most widely used means for the assessment of fetal condition. CTG consists of two traces one depicting the Fetal Heart Rate (FHR), and the other the Uterine Contractions (UC) activity. Many automatic methods have been proposed for the interpretation of the CTG. Most of them rely either on a binary classification approach or on a multiclass approach to come up with a decision about the class that the tracing belongs to. This work investigates the use of a one-class approach to the assessment of CTGs building a model only for the healthy data. The preliminary results are promising indicating that normal traces could be used as part of an automatic system that can detect deviations from normality.

  • 56.
    Georgoulas, George
    et al.
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi, Department of Computer Engineering, TEI of Epirus, Arta.
    Karvelis, Petros
    Department of Computer Engineering, TEI of Epirus, Arta.
    Stylios, Chrysostomos
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi, Department of Computer Engineering, TEI of Epirus, Arta.
    Tsoumas, Ioannis
    Siemens Industry Sector, Automation and Drives, Large Drives, Nuremberg, Germany, Large Drives, Products R&D Department, Siemes Inustry.
    Antonino-Daviu, Jose Alfonso
    Institute of Energy Engineering, University of Valencia, Spain.
    Hernandez, Jesus Corral
    Institute of Energy Engineering, University of Valencia, Spain.
    Alarcon, Vicente Climente
    Department of Electrical Engineering and Automation, Aalto University, Espoo.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Automatizing the detection of rotor failures in induction motors operated via soft-starters2016In: Annual Conference of the IEEE Industrial Electronics Society, IECON 2015: Yokohama, Japan, 9-12 Nov. 2015, Piscataway, NJ: IEEE Communications Society, 2016, p. 3743-3748, article id 7392684Conference paper (Refereed)
    Abstract [en]

    Implementation of unsupervised induction motor condition monitoring systems has drawn an increasing attention recently among motor drives manufacturers. In the case of soft- starters the possibility of incorporating fault detection features to their conventional functions provides an added value to those elements. Design and development of advanced algorithms that are able to automatically detect and alert about possible failures without requiring continuous human inspection is an especially challenging research goal. In this paper, an algorithm for the automatic detection of rotor damages in induction motors in the case of soft starting is proposed. The twofold approach relies, first, on the application of a time-frequency transform to the starting current signal and, second, on a pattern recognition stage based on the treatment of the time-frequency representation as a symbolic sequence. The innovation of this work is the implementation of the proposed approach for the automatic detection of rotor cage faults in soft-started motors. The experimental results prove the usefulness of the approach for the automatic detection of such faults and its potential for possible future implementation in soft-started machines.

  • 57.
    Georgoulas, George
    et al.
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi.
    Mustafa, Mohammed Obaid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Tsoumas, Ioannis
    Siemens Industry Sector, Automation and Drives, Large Drives, Nuremberg, Germany.
    Daviu, Antonino
    Instituto de Ingeniera Energtica, Universitat Politcnica de Valncia, 46022 Valencia.
    Alarcon, Climente
    Instituto de Ingeniera Energtica, Universitat Politcnica de Valncia, 46022 Valencia.
    Stylios, Chrysostomos
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Principal component analysis of the start-up transient and hidden Markov modeling for broken rotor bar fault diagnosis in asynchronous machines2013In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 40, no 17, p. 7024-7033Article in journal (Refereed)
    Abstract [en]

    This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMM, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.

  • 58.
    Georgoulas, George
    et al.
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi, Department of Computer Engineering, TEI of Epirus, Arta.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mustafa, Mohammed Obaid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Data Fusion Approach to Bearing Fault Detection and Diagnosis2015In: IEEE International Conference on Power Engineering, Energy and Electrical Drives, POWERENG 2015, May 11-13, Riga, Latvia, 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 109-113Conference paper (Refereed)
    Abstract [en]

    This paper presents a data fusion approach for the diagnosis of bearing faults under different seeded fault scenarios. The approach is based on the extraction of three simple and intuitive features that fuse the information that comes from two accelerometers placed at two different sites of the test bed. The analysis shows that in the case of the occurrence of a fault even in an early stage the “footprint” left at the scatter plot of the measurements coming from the two accelerometers can effectively turned into features/descriptors by simple statistical measures such as the elements of the covariance matrix. Those features when fed to a k-nearest neighbor classifier or an ensemble of one class detectors can lead to a remarkably high detection/diagnostic performance.

  • 59.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Alarcon, Vicente Climente
    Department of Electrical Engineering and Automation, Aalto University, Espoo.
    Dritsas, Leonidas
    University of Patras, Department of Electrical Engineering.
    Antonino-Daviu, Jose Alfonso
    Institute of Energy Engineering, University of Valencia, Spain.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Start-up analysis methods for the diagnosis of rotor asymmetries in induction motors-seeing is believing2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 372-377, article id 7536045Conference paper (Refereed)
    Abstract [en]

    This article presents a qualitative analysis of different methods proposed for the diagnosis of broken rotor bars using the stator current during start-up operation. The slip dependent components, caused by the asymmetry, which is created by the breakage of rotor bar(s) and especially the left sideband harmonic (LSH) component, can create a distinctive pattern in a time- frequency plane. Short Time Fourier Transform, Wavelet analysis, and Winger-Ville Distribution are evaluated by using signals coming from motors operating in real industrial settings. The corresponding analysis presents the pros and the cons of these approaches for their potential application under realistic industrial conditions using the larger number of real life cases encountered in the literature.

  • 60.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Climente-Alarcón, Vicente
    Department of Electrical Engineering and Automation, Aalto University.
    Antonino-Daviu, José Alfonso
    Instituto Tecnologico de la Energia, Universitat Politècnica de València.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Arkkio, Antero
    Department of Electrical Engineering and Automation, Aalto University.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Multi-label Classification Approach for the Detection of Broken Bars and Mixed Eccentricity Faults Using the Start-up Transient2017In: IEEE International Conference on Industrial Informatics (INDIN), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 430-433, article id 7819198Conference paper (Refereed)
    Abstract [en]

    In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault, using the power-set approach. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity. For the feature extraction stage, the time-frequency representation, resulting from the application of the short time Fourier transform of the start-up current is exploited. The proposed approach is validated using simulation data with promising results.

    The full text will be freely available from 2019-01-19 15:08
  • 61.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Climente-Alarcón, Vicente
    Department of Electrical Engineering and Automation, Aalto University.
    Antonino-Daviu, José Alfonso
    Instituto Tecnologico de la Energia, Universitat Politècnica de València.
    Tsoumas, Ioannis P.
    ABB Corporate Research, Baden-Dättwil.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Arkkio, Antero
    Department of Electrical Engineering and Automation, Aalto University.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    The use of a multilabel classification framework for the detection of broken bars and mixed eccentricity faults based on the start-up transient2017In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 13, no 2, p. 625-634, article id 7778161Article in journal (Refereed)
    Abstract [en]

    In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific fault. The faulty conditions examined include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three 'problem transformation' methods are tested and compared. For the feature extraction stage, the start-up current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multilabel framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation

    The full text will be freely available from 2018-12-08 15:23
  • 62.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kappatos, V.
    Department of Technology and Innovation (ITI), University of Southern Denmark (SDU).
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Acoustic emission localization on ship hull structures using a deep learning approach2016In: Vibroengineering Procedia, ISSN 2345-0533, Vol. 9, p. 56-61Article in journal (Refereed)
    Abstract [en]

    this paper, deep belief networks were used for localization of acoustic emission events on ship hull structures. In order to avoid complex and time consuming implementations, the proposed approach uses a simple feature extraction module, which significantly reduces the extremely high dimensionality of the raw signals/data. In simulation experiments, where a stiffened plate model was partially sunk into the water, the localization rate of acoustic emission events in a noise-free environment is greater than 94 %, using only a single sensor

  • 63.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Gavrilis, Dimitris
    Department of Electrical Engineering and Computer Technology, University of Patras.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An ordinal classification approach for CTG categorization2017In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Piscataway, NJ: IEEE, 2017, p. 2642-2645, article id 8037400Conference paper (Refereed)
    Abstract [en]

    Evaluation of cardiotocogram (CTG) is a standard approach employed during pregnancy and delivery. But, its interpretation requires high level expertise to decide whether the recording is Normal, Suspicious or Pathological. Therefore, a number of attempts have been carried out over the past three decades for development automated sophisticated systems. These systems are usually (multiclass) classification systems that assign a category to the respective CTG. However most of these systems usually do not take into consideration the natural ordering of the categories associated with CTG recordings. In this work, an algorithm that explicitly takes into consideration the ordering of CTG categories, based on binary decomposition method, is investigated. Achieved results, using as a base classifier the C4.5 decision tree classifier, prove that the ordinal classification approach is marginally better than the traditional multiclass classification approach, which utilizes the standard C4.5 algorithm for several performance criteria.

  • 64.
    Georgoulas, Georgios
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bearing fault detection and diagnosis by fusing vibration data2016In: IECON Proceedings (Industrial Electronics Conference), Piscataway, NJ: IEEE Computer Society, 2016, p. 6955-6960, article id 7794118Conference paper (Refereed)
    Abstract [en]

    This article presents a simple method for the detection and diagnosis of bearing faults, by fusing the information coming from two accelerometers. The method relies on three simple and intuitive features, extracted from the data coming from accelerometers placed at two different sites of the system under investigation. Our preliminary results indicate that by using simple statistical measures, such as the elements of the covariance matrix of the two sensors, faults at an early stage can be detected. In our the proposed scheme, the extracted features are fed to a k-nearest neighbour classifier for diagnosis purposes or to an ensemble of one-class detectors, if only the information from normal situation is available. As it is proved, based on experimental results, in both scenarios a remarkably high detection/diagnostic performance is achieved.

    The full text will be freely available from 2018-12-22 15:12
  • 65.
    Giannakas, Theodoros
    et al.
    University of Patras.
    Andrikopoulos, Georgios
    Department of Electrical and Computer Science, University of Patras.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Manesis, Stamatis
    Department of Electrical and Computer Engineering, University of Patras.
    On Energy Optimization of a Pulp and Paper Refiner based on Model Predictive Control2015Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to investigate and examine the modeling and control problem of the papermaking’s sub-process also known as pulp refining. The existing modeling approaches developed for the pulp and paper refining process are being investigated, while the most important model approximations, extracted from the existing related literature, are being simulated and examined in detail. The main goal of the article is to determine whether the modeling approaches of the pulp and paper refining process can be successfully controlled via a Model Predictive Control (MPC) based structure and at which extend this could lead in a better control scheme and in an overall energy optimization. Extensive simulation trials are being carried out, where the MPC parameters are being fine-tuned through trial-and-error sequences in order for examining the overall performance in controlling the various modeling approaches of the process. In order to further evaluate the efficacy of the proposed control scheme, the MPC related results are being compared to experimental data extracted from a real refining system that utilized a generic industrial controller.

  • 66.
    Gkikopouli, Andrianna
    et al.
    University of Patras.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Manesis, Stamatis
    University of Patras.
    A survey on underwater wireless sensor networks and applications2012In: 2012 20th Mediterranean Conference on Control & Automation: Barcelona, Spain, July 3-6, 2012, Piscataway, NJ: IEEE Communications Society, 2012, p. 1147-1154Conference paper (Refereed)
    Abstract [en]

    In this article a survey on the different technologies in the area of Underwater Wireless Sensor Networks (UWSN) will be presented. The characteristics of these networks are different from those found in the terrestrial ones, while their architecture is vulnerable to various issues such as large propagation delays, mobility of floating sensor nodes, limited link capacity and multiple messages receptions due to reflections on the sea ground and sea surface. This article will present an overview of the underlying technologies in UWSN and will focus in presenting the most important research approaches towards UWSNs’ architecture, routing, MAC and localization protocols, energy consumption and security, while highlighting their most illustrative real-life applications.

  • 67.
    Goldin, E.
    et al.
    GSTAT, Israel.
    Feldman, D.
    GSTAT, Israel.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Castaño Arranz, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cloud computing for big data analytics in the Process Control Industry2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1373-1378, article id 7984310Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0

  • 68.
    Herceg, Domagoj
    et al.
    IMT School for Advanced Studies Lucca.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics.
    Castaño Arranz, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Patrinos, Panagiotis K.
    KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics.
    Bemporad, Alberto
    IMT School for Advanced Studies Lucca.
    Niemi, Jan
    Swerea MEFOS, Box 812, Lulea.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1361-1366, article id 7984308Conference paper (Refereed)
    Abstract [en]

    The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions

  • 69.
    Jafari, Hedyeh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Castaño Arranz, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Control Structure Design for a Walking Beam Furnace2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1355-1360, article id 7984307Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to introduce a novel sparse controller design for the temperature control of an experimental walking beam furnace in steel industry. Adequate tracking of temperature references is essential for the quality of the heated slabs. However, the design of the temperature control is hindered by the multivariable (non-square) dynamic behavior of the furnace. These dynamics include significant loop interactions and time delays. Furthermore, a novel data-driven model, based on real life experimental data that relies on a subspace state representation in a closed loop approach is introduced. In the sequel, the derived model is utilized to investigate the controller's structure. By applying the relative gain array approach a decentralized feedback controller is designed. However, in spite of the optimal and sparse design of the controller, there exists interaction between loops. By analyzing the interaction between the inputs-outputs with the Σ2 Gramian-based interaction methodology, a decoupled multi-variable controller is implied. The simulation result, based on the experimental modeling of the furnace, shows that the controller can successfully decrease the interaction between the loops and track the reference temperature set-points.

    The full text will be freely available from 2019-07-20 15:31
  • 70.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Autonomous Surveying of Underground Mine using MAVs geogeo2019Conference paper (Refereed)
    Abstract [en]

    Micro Aerial Vehicles (MAVs) are platforms that received great attention during the last decade. Recently, the mining industry has been considering the usage of aerial autonomous platforms in their processes. This article initially investigates potential application scenarios for this technology in mining. Moreover, one of the main tasks refer to surveillance and maintenance of infrastructure assets. Employing these robots for underground surveillance processes of areas like shafts, tunnels or large voids after blasting, requires among others the development of elaborate navigation modules. This paper proposes a method to assist the navigation capabilities of MAVs in challenging mine environments, like tunnels and vertical shafts. The proposed method considers the use of Potential Fields method, tailored to implement a sense-and-avoid system using a minimal ultrasound-based sensory system. Simulation results demonstrate the effectiveness of the proposed strategy.

  • 71.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Dynamic visual sensing based on MPC controlled UAVs2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1201-1206, article id 7984281Conference paper (Refereed)
    Abstract [en]

    This article considers the establishment of a dynamic visual sensor from monocular cameras to enable a reconfigurable environmental perception. The cameras are mounted on Micro Aerial Vehicles (MAV) which are coordinated by a Model Predictive Control (MPC) scheme to retain overlapping field of views and form a global sensor with varying baseline. The specific merits of the proposed scheme are: a) the ability to form a configurable stereo rig, according to the application needs, and b) the simple design, the reduction of the payload and the corresponding cost. Moreover, the proposed configurable sensor provides a glpobal 3D reconstruction of the surrounding area, based on a modified Structure from Motion approach. The efficiency of the suggested flexible visual sensor is demonstrated in simulation results that highlight the novel concept of cooperative flying cameras and their 3D reconstruction capabilities

    The full text will be freely available from 2019-07-20 13:11
  • 72.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A robust reconfigurable control scheme against pose estimation induced time delays2016In: 2016 IEEE Conference on Control Applications (CCA): 19-22 Sept. 2016, Piscataway, NU: IEEE Communications Society, 2016, p. 581-586, article id 7587892Conference paper (Refereed)
    Abstract [en]

    Time delays are one of the most common problems when utilizing a visual sensor for pose estimation or navigation in aerial robotics. Such time delays can grow exponentially as a function of the scene's complexity and the size of the mapping during classical Simultaneous Localization and Mapping (SLAM) strategies. In this article, a robust reconfigurable control scheme against pose estimation induced time delays is presented. Initially, an experimental verification of the induced time delays via pose estimation is performed for the attitude problem of a hexacopter, while a switching time delay dependent modeling approach is formulated. In addition, a stability analysis algorithm is introduced in order to evaluate the maximum allowable time delays that the target system can handle for a given LQR controller. The varying nature of the time delays results in a switching system with the latency time to play the role of a switching rule, while simulation results are presented to outline the effects of the time-induced delays in hexarotor-based systems and finally evaluate the overall efficiency of the proposed control scheme.

  • 73.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Evaluation of Visual Localization Systems in Underground Mining2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 539-544, article id 7535853Conference paper (Refereed)
    Abstract [en]

    In this article an evaluation of the current technology on visual localization systems for underground mining is presented. The proposed study is considered to be the first step among others towards enabling the vision of underground localization for Unmanned Micro Aerial Vehicles. Furthermore, the aim of this article, is to verify applicable and reliable low cost existing methods and technologies for the problem of UAV localization in underground, harsh mining environments and more specifically in one of the biggest mines in Europe, the iron ore mine of LKAB in Kiruna, Sweden. In the experimental trials, the sensors employed were a RGB-D camera, a Kinect 2 and a Playstation 3 Eye web camera used in two configurations, as a stereo rig and as a monocular visual sensor. The processing of the stored data from the experiments will provide an insight for the applicability of these sensors, while it will identify what further technological and research developments are required in order to develop affordable autonomous UAV solutions for improving the underground mining production processes.

  • 74.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Survey on Computer Vision for UAVs: Current Developments and Trends2017In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 87, no 1, p. 141-168Article in journal (Refereed)
    Abstract [en]

    During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.

  • 75.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Terreran, Matteo
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On vision enabled aerial manipulation for multirotors2018In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    This article presents an integrated vision-based guiding system for aerial manipulation. More specifically, a 4 DoF planar dexterous manipulator, with a stereo camera attached on the end-effector, is endowed to a multirotor aerial platform enabling active manipulation capabilities. The proposed novel approach combines a visual processing scheme for object detection and tracking, as well as a manipulator positioning for allowing the aerial platform to approach the surface of interaction efficiently. In the developed scheme, the object detection is based on correlation filters to track the target robustly, while the depth information, from the stereo camera on board the manipulator, is used to extract the centroid of the manipulated object, compute its relative configuration with respect to the UAV and align the end-effector properly with the grasping point. The effectiveness of the proposed scheme is demonstrated in multiple experimental trials and simulations, highlighting it's applicability towards autonomous aerial manipulation.

    The full text will be freely available from 2020-01-08 13:21
  • 76.
    Karvelis, Petros
    et al.
    Department of Computer Engineering, TEI of Epirus, Arta.
    Georgoulas, George
    Department of Computer Engineering, TEI of Epirus, Arta.
    Stylios, Chrysostomos
    Department of Computer Engineering, TEI of Epirus, Arta.
    Tsoumas, Ioannis
    Large Drives, Products R&D Department, Siemes Inustry.
    Antonino-Daviu, Jose Alfonso
    Institute of Energy Engineering, University of Valencia, Spain.
    Hernandez, Jesus Corral
    Institute of Energy Engineering, University of Valencia, Spain.
    Alarcon, Vicente Climente
    Department of Electrical Engineering and Automation, Aalto University, Espoo.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Symbolic time series analysis of the soft starting transient in induction machines2015In: 2015 IEEE International Conference on Industrial Technology (ICIT 2015) to be held in Seville, Spain, March 17-19, 2015.: Seville, Spain, March 17-19, 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 3243-3248Conference paper (Refereed)
    Abstract [en]

    Induction motors are in the heart of almost every production line especially due to their robustness under harsh environments. Nevertheless, even induction machines are prone to faults. Among them, the faults related to the breakage of rotor bars have received special attention by the research community with a number of methods proposed both for the case of steady state as well as for transient operation. For the latter, methods relying on the analysis of the start-up transient have proven to be able to effectively isolate the faulty component that is created by the asymmetry caused by the bar breakage. However, very little work has been done concerning the soft starting of induction machines. In this work, preliminary results of the application of a symbolic time series technique for the analysis of the transient, when the motor is controlled by a soft starter, will be presented and experimentally evaluated.

  • 77.
    Karvelis, Petros
    et al.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Faleij, Ragnar
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Georgoulas, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Laser Dot Tracking Method for the Assessment of Sensorimotor Function of the Hand2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway. NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 217-222, article id 7984121Conference paper (Refereed)
    Abstract [en]

    Assessment of sensorimotor function is crucial during the rehabilitation process of various physical disorders, including impairments of the hand. While moment performance can be accurately assessed in movement science laboratories involving highly specialized personnel and facilities there is a lack of feasible objective methods for the general clinic. This paper describes a novel approach to sensorimotor assessment using an intuitive test and a specifically tailored image processing pipeline for the quantification of the test. More specifically the test relies on the patient being instructed on following a zig-zag pattern using a handled laser pointer. The movement of the pointer is tracked using image processing algorithm capable of automating the whole procedure. The method has potential for feasible objective clinical assessment of the hand and other body parts

  • 78.
    Kelasidi, Eleni
    et al.
    Electrical and Computer Engineering Department, University of Patras.
    Andrikopoulos, Georgios
    Department of Electrical and Computer Science, University of Patras.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Manesis, Stamatis
    Electrical and Computer Engineering Department, University of Patras.
    A survey on pneumatic muscle actuators modeling2012In: Journal of Energy and Power Engineering, ISSN 1934-8975, E-ISSN 1934-8983, Vol. 6, no 9, p. 1442-1452Article in journal (Refereed)
    Abstract [en]

    The aim of this article is to provide a survey on the most popular modeling approaches for Pneumatic Muscle Actuators (PMAs). PMAs are highly non-linear pneumatic actuators where their elongation is proportional to the interval pressure. During the last decade, there has been an increase in the industrial and scientific utilization of PMAs, due to their advantages such as high strength and small weight, while various types of PMAs with different technical characteristics have been appeared in the literature. This article will: a) analyse the PMA's operation from a mathematical modeling perspective, b) present their merits and drawbacks of the most common PMAs, and c) establish the fundamental basis for developing industrial applications and conducting research in this field.

  • 79.
    Lindqvist, Adrian
    et al.
    Luleå University of Technology.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Optimal Design and Modeling of a Tilt Wing Aircraft2015In: IEEE Mediterranean Conference on Control and Automation, Torremolinos, Spain, June 16-19, 2015 / [ed] V. Munoz, Piscataway, NJ: IEEE Communications Society, 2015, p. 701-708, article id 7158828Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to present an optimal model for the behavior of a tilt wing aircraft during its transition state. A tilt wing aircraft is a hybrid between a helicopter and a plane, which has the capabilities of both parts, meaning that it can stand still and hover and by tilting its wing 90 degrees to act like a regular plane. Overall, a tilt rotor aircraft has the extended merits of vertical take offs and landings, to stand still and hover in mid air, while being able to travel in long distances efficiently. The novelty of this article stems from: a) the analysis of the aircraft during the transition between helicopter mode and plane for the angles between 0-90 degrees, b) the comparison between the model extracted from the simulations and tests from a wind tunnel, and c) the proposal for an optimal design for a tilt wing UAV. The efficiency of the proposed modeling approach has been evaluated in multiple simulated and experimental verification.

  • 80.
    Mamikoglu, Umut
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Andrikopoulos, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Pauelsen, Mascha
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Electromyography Based Joint Angle Estimation and Control of a Robotic Leg2016In: 6th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob 2016): June 26-29, Singapore, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 182-187, article id 7523619Conference paper (Refereed)
    Abstract [en]

    Musculoskeletal modeling based on Electromyography (EMG) has many applications in physiotherapy and biologically-inspired robotics. In this article, a novel methodology for the modeling of the dynamics of an antagonistic muscle pair that actuates the human ankle joint movements will be established. As it will be presented, the musculoskeletal model is based on a multi input single output (MISO) auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes the integrated EMG measurements as input and estimates the corresponding joint angles. Based on this methodology, a Pneumatic Artificial Muscle (PAM) robotic leg setup that mimics the flexion/extension movement of human ankle joint is controlled to replicate the human movement. The experimental results demonstrate the performance of EMG based joint angle estimation and control of the robotic leg with the proposed model.

  • 81.
    Mamikoglu, Umut
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pauelsen, Mascha
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Varagnolo, Damiano
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehab.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Elbow Joint Angle Estimation by Using Integrated Surface Electromyography2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 785-790, article id 7535891Conference paper (Refereed)
    Abstract [en]

    Electromyography (EMG) signals represent the electrical activation of skeletal muscles and contain valuable information about muscular activity. Estimation of the joint movements by using surface EMG signals has great importance as a bio-inspired approach for the control of robotic limbs and prosthetics. However interpreting surface EMG measurements is challenging due to the nonlinearity and user dependency of the muscle dynamics. Hence it requires complex computational methods to map the EMG signals and corresponding limb motions. To solve this challenge we here propose to use an integrated EMG signal to identify the EMG-joint angle relation instead of using common EMG processing techniques. Then we estimate the joint angles for elbow flexion-extension movement by using an auto-regressive integrated moving average with exogenous input (ARIMAX) model, which takes integrated EMG measurements as input. The experiments showed that the suggested approach results in a 21.85% average increase in the estimation performance of the elbow joint angle compared to the standard EMG processing and identification.

  • 82.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the covering of a polygonal region with fixed size rectangles with an application towards aerial inspection2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1316-1320, article id 7984300Conference paper (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAVs) equipped with remote visual sensing can be used in wide range of applications. However, guaranteeing the full coverage of the area and translating this coverage in a path planning problem, it is a quite challenging task. Thus, in this article a well-known and well-investigated family of hard optimization problems, covering a polygonal region (target area) with fixed size rectangles (camera frustrum), is studied. The problem is formulated mathematically and solved using metaheuristic optimization algorithms. The proposed novel algorithmic scheme requires an a priori 2D model of the target area, while it tries to maximize the coverage with a minimum number of fixed size rectangles. Finally, multiple simulation results are presented that prove the efficacy of the proposed scheme

    The full text will be freely available from 2019-07-20 10:53
  • 83.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooperative coverage path planning for visual inspection2018In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 74, p. 118-131Article in journal (Refereed)
    Abstract [en]

    This article addresses the inspection problem of a complex 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). The main novelty of the proposed scheme stems from the establishment of a theoretical framework capable of providing a path for accomplishing a full coverage of the infrastructure, without any further simplifications (number of considered representation points), by slicing it by horizontal planes to identify branches and assign specific areas to each agent as a solution to an overall optimization problem. Furthermore, the image streams collected during the coverage task are post-processed using Structure from Motion, stereo SLAM and mesh reconstruction algorithms, while the resulting 3D mesh can be used for further visual inspection purposes. The performance of the proposed Collaborative-Coverage Path Planning (C-CPP) has been experimentally evaluated in multiple indoor and realistic outdoor infrastructure inspection experiments and as such it is also contributing significantly towards real life applications for UAVs.

    The full text will be freely available from 2020-05-01 11:28
  • 84.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooperative UAVs as a tool for Aerial Inspection of the Aging Infrastructure2017In: Field and Service Robotics: Results of the 11th International Conference / [ed] Marco Hutter, Roland Siegwart, Cham: Springer, 2017, p. 177-189Conference paper (Refereed)
    Abstract [en]

    This article presents an aerial tool towards the autonomous cooperative coverage and inspection of a 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). In the presented approach the UAVs are relying only on their onboard computer and sensory system, deployed for inspection of the 3D structure. In this application each agent covers a different part of the scene autonomously, while avoiding collisions. The visual information collected from the aerial team is collaboratively processed to create the 3D model. The performance of the overall setup has been experimentally evaluated in a realistic outdoor infrastructure inspection experiments, providing sparse and dense 3D reconstruction of the inspected structures.

  • 85.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    2D visual area coverage and path planning coupled with camera footprints2018In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 75, p. 1-16Article in journal (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAVs) equipped with visual sensors are widely used in area coverage missions. Guaranteeing full coverage coupled with camera footprint is one of the most challenging tasks, thus, in the presented novel approach a coverage path planner for the inspection of 2D areas is established, a 3 Degree of Freedom (DoF) camera movement is considered and the shortest path from the taking off to the landing station is generated, while covering the target area. The proposed scheme requires a priori information about the boundaries of the target area and generates the paths in an offline process. The efficacy and the overall performance of the proposed method has been experimentally evaluated in multiple indoor inspection experiments with convex and non convex areas. Furthermore, the image streams collected during the coverage tasks were post-processed using image stitching for obtaining a single overview of the covered scene.

    The full text will be freely available from 2020-06-01 11:15
  • 86.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Remaining Useful Battery Life Prediction for UAVs based on Machine Learning2017In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 4727-4732Article in journal (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions.

  • 87.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Distributed Model Predictive Control for Unmanned Aerial Vehicles2015In: 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems: RED-UAS 2015, Cancun, Mexico, 23 - 25 November 2015, Picataway, NJ: IEEE Communications Society, 2015, p. 152-161, article id 7441002Conference paper (Refereed)
    Abstract [en]

    In this article a distributed model pre- dictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is be- ing presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two different control ar- chitectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the efficiency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

  • 88.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Bearing fault classification based on minimum volume ellipsoid feature extraction2013In: 2013 IEEE Multiconference on Systems and Control (MSC), Hyderabad, India, August, 28-30, 2013, 2013, p. 1177-1182Conference paper (Refereed)
    Abstract [en]

    This article presents a novel fault classification and diagnosis technique for bearings based on a Minimum Volume Ellipsoid (MVE) method for feature extraction. Data from two accelerometers located at two different sights of the test bed are combined to create a two dimensional representation and the feature extraction stage condenses that information using an ellipsoid description. The proposed features feed a simple non-linear classifier which separates almost perfectly between normal and faulty conditions, with also very high diagnostic accuracy between the faulty classes. The obtained results suggest that this novel representation can be used within a condition monitoring system.

  • 89.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Principal Component Analysis Anomaly Detector for Rotor Broken Bars2015In: IECON 2014: 40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, USA , Oct. 29 2014 - Nov. 1 2014, Piscataway, NJ: IEEE Communications Society, 2015, p. 3462-3467Conference paper (Refereed)
    Abstract [en]

    In this article a method for the detection of broken rotor bars in asynchronous machines operating under full load is presented. Unlike most Motor Current Signature Analysis (MCSA) approaches, which operate in the frequency domain, our method operates in the time domain. The scheme is based on the use of a Principal Component Analysis (PCA) fault/anomaly detector applied on the three stator currents to calculate the Q statistic which is employed for detecting a fault. The efficiency of the proposed scheme was experimentally evaluated using different fault severity levels, ranging from 1/4 of a broken bar to three broken bars. The obtained results indicate that the method can detect the caused asymmetry with a very restricted amount of data.

  • 90.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Stator winding short circuit fault detection based on uncertainty ellipsoid intersection for three phase induction motors2013In: 10th International Conference on Informatics in Control, Automation and Robotics: Reykjavik, Iceland, 29-31, July, 2013, 2013, p. 206-212Conference paper (Refereed)
    Abstract [en]

    In this article a fault detection scheme for different percentage of stator winding short circuit is presented for three phase induction motors. In the examined case, the induction motor in the faulty and healthy case has been transformed in the two phase (q−d) model. The model has been identified by the utilization of a Least Squares Set Membership Identification (SMI) algorithm, where additional to the identified parameters, confidence intervals can be also calculated, based on a priori knowledge for the corrupting measurement noise. The identified confidence intervals in an μ–dimensional space can be represented as hyper–ellipsoids having as a center the identified parameters’ vector. The novelty of this article stems from the proposal of a fast and geometrical based scheme, which relies on the calculation of the distance among centers of hyper–ellipsoids and the corresponding intersection in each iteration of the identification procedure. Detailed analysis of the proposed fault detection strategy, as also extended simulation results are being presented that prove the efficiency of the suggested scheme.

  • 91.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Department of Informatics and Communications Technology, Technical Educational Institute of Epirus, 47100 Artas, Kostakioi, Department of Computer Engineering, TEI of Epirus, Arta.
    Fault classification of Broken Rotor Bars in Induction Motors Based on Envelope Current Analysis2015In: IEEE 13th International Conference on Industrial Informatics (INDIN), 2015: Cambridge, United Kingdom, 22-24 July 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 795-800, article id 7281838Conference paper (Refereed)
    Abstract [en]

    In this article a method for the fault classification of one, two, and three broken bars in induction motors under full load condition is presented. The proposed methodology is based on the current envelope analysis, which in the past has been also widely utilized in analyzing the rotor faults at low slips. As it will be presented, the information obtained from the envelope current is valuable in manifesting and validating the presence of fault, since the current envelope and its characteristics often contains important information about the existence of a fault and the corresponding fault type. The proposed method mainly focuses on the case of steady-state operation under full load. In the established fault detection scheme, from the stator’s current six statistical features are extracted and utilized for the fault detection and classification. In more detail, three classifiers, a linear, a quadratic and a Nearest Neighbor have been investigated for the diagnosis of broken rotor bar faults of an induction motor. The presented approach have manifested promising results using experimental data.

  • 92.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A fault diagnosis scheme for three phase induction motors based on uncertainty bounds2012In: IECON 2012: 38th Annual Conference of the IEEE Industrial Electronics Society, Piscataway, NJ: IEEE Communications Society, 2012, p. 1596-1601Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to present a fault diagnosis scheme for the case of squirrel–cage Three Phase Induction Motors based on uncertainty bounds violation conditions. The suggested scheme has the capability to diagnose two types of faults: a) broken rotor bar and b) short circuit in stator winding. The fault diagnosis is being performed through a two steps procedure. In the first step the parameters of the healthy induction motor are being identified by utilizing a Set Membership Identification approach, where corresponding uncertainty bounds are also being provided. In the second step, specific proposed bound violation conditions for the fault detection and fault diagnosis are being on–line evaluated during a sliding time window. Multiple simulation results are being presented that prove the efficacy of the proposed scheme towards fault detection and fault diagnosis.

  • 93.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Robust Linear Discrimination Fault Classification Scheme for Three Phase Induction Motors2013In: CoDIT 2013: IEEE 2013 International Conference on Control, Decision and Information Technologies, Piscataway, NJ: IEEE Communications Society, 2013, p. 524-529Conference paper (Refereed)
    Abstract [en]

    In this article a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park’s vector pattern. The proposed methodology has the merit to diagnose different types of faults such as: a) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. Robust linear discrimination has been one of the most widely used fault detection method in real life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time requires a straight forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results for different fault types.

  • 94.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A survey on modeling approaches for three phase induction motors2012In: Proceedings of the IASTED International Conference on Modelling, Simulation and Identification / [ed] T. Znati; Z. Bar-Joseph; L. Rothrock, ACTA Press, 2012, p. 336-343Conference paper (Refereed)
    Abstract [en]

    Three–phase induction machines are generally being utilized as motors for many industrial systems, mainly due to their simple construction, low cost and other merits, when compared with other types of motors. The main aim of this article is to present a survey regarding the appeared approaches towards the mathematical modeling of three phase induction motors, which is the first step before designing appropriate control schemes. More analytically, two main modeling approaches will be presented: a) the complete three phase models, and b) the simplified quadra-ture phase models. Within these two main categories, all the types of the modeling approaches for induction motors will be presented, while the relevant significant applications, including the corresponding advantages and disadvantages for these models will be also presented. Finally, an extended bibliography is being provided as a base line for future investigations.

  • 95.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Broken Bars Fault Diagnosis Based on Uncertainty Bounds Violation for Three Phase Induction Motors2015In: International Studies in Religion and Society, ISSN 1530-1311, E-ISSN 2050-7038, p. 304-325Article in journal (Refereed)
    Abstract [en]

    In this article, a novel fault diagnosis scheme, based on uncertainty bounds violation, is being presented for the case of broken bars in squirrel–cage Three Phase Induction Motors. The fault diagnosis is done in two steps. Firstly the parameters of the healthy induction motor are identified using a set member- ship identification (SMI) approach, where corresponding uncertainty bounds are also being provided. Secondly the proposed uncertainty bounds violation conditions for the fault diagnosis are evaluated on–line, on the converged identified model, during a sliding time window. Multiple simulation results are presented that demonstrate the efficacy of the proposed scheme towards fault detection and diagnosis among different number of broken bars.

  • 96.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Broken rotor bar fault detection based on uncertainty ellipsoidal intersection for three phase induction motors2013In: 21st IEEE Mediterranean Conference on Control and Automation, June 25-28, Platanias, Chania, Crete, Greece, 2013., Piscataway, NJ: IEEE Communications Society, 2013, p. 388-393, article id 6608751Conference paper (Refereed)
    Abstract [en]

    In this article a fault detection scheme for broken rotor bar fault detection in three phase induction motor is presented. In the proposed scheme the induction motor has been transformed in the equivalent two phase (q−d) space, while the modeling of the faulty case has been also formulated. The model has been identified by the utilization of the Set Membership Identification (SMI) algorithm that has the merit of identifying both the parameters of the motor as also providing uncertainty bounds in both the healthy and the faulty cases. Based on the adopted methodology, the uncertainty bounds and the corresponding identified parameters of the induction motor is presented as 3D–ellipsoids, while a novel fast and efficient fault detection scheme has been proposed that is able to track iteratively the ellipsoid centers, the distance among centers, the intersection between the initial and a priori known converged states of the motor and the current ones, before or after the fault occurrence. Detailed analysis of the proposed approach and he fault detection strategy, as also extended simulation results are being presented that prove the efficiency of the suggested scheme.

  • 97.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Experimental evaluation of a broken rotor bar fault detection scheme based on uncertainty bounds violation2013In: 39th Annual Conference of the IEEE Industrial Electronics Society: November 10-13, 2013, Vienna, Austria, IEEE Communications Society, 2013, p. 5541-5546Conference paper (Refereed)
    Abstract [en]

    In this article an experimental evaluation of a broken rotor bar fault detection scheme based on uncertainty bounds violation will be presented. The novelty of this article stems from the establishment and the experimental evaluation of fault detection scheme being able to detect faults at the beginning of its occurrence, based on Set Membership Identification and novel proposed boundary violation rules for the identified motor’s parameters. By the utilization of the SMI technique, the simplified equivalent model of the induction motor is being identified during the steady state operation (non–fault case), while at the same time safety bounds for the identified variables are being provided, based on an a priori defined corrupting additive noise. On the event of a fault, specific fault detection conditions are being proposed that can capture the fault of a broken bar. Detailed analysis of the proposed approach as also extended experimental results are being presented that prove the efficiency of the proposed scheme.

  • 98.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fault classification scheme for three phase induction motor2014In: International Journal of System Dynamics Applications, ISSN 2160-9772, E-ISSN 2160-9799, Vol. 3, no 1Article in journal (Refereed)
    Abstract [en]

    In this article a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park’s vector pattern. The proposed methodology has the merit to diagnose different types of faults such as: a) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. Robust linear discrimination has been one of the most widely used fault detection method in real life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time applies a straight forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results for different fault types.

  • 99.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fault detection based on set membership identification for three phase induction motors2012Conference paper (Refereed)
    Abstract [en]

    In this article a fault detection scheme for stator winding short circuit fault detection in the case of a three phase induction motor is being presented. The three phase motor is model in (q-d) model space for the normal and the faulty case. The motor is being identified by the utilization of Set Membership Identification (SMI) that has the merit of identifying both the parameters of the motor as also providing uncertainty safety bounds by calculating orthotopes which bounds the system’s parameter vector. Based on the volume and the trend of these orthotopes, rules for identifying the existence of a fault are being presented. If the current values of the identified parameters do not lie inside the safety bounds in the healthy case, but lie in an area that is being defined by the model of the short circuit case, then a fault is being triggered.

  • 100.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Stator winding short circuit fault detection based on set membership identification for three phase induction motors2012In: 2012 20th Mediterranean Conference on Control and Automation: Barcelona, Spain, July 3-6, 2012, Piscataway, NJ: IEEE Communications Society, 2012, p. 290-296Conference paper (Refereed)
    Abstract [en]

    In this article a fault detection scheme for stator winding short circuit fault detection in the case of a three phase induction motor is being presented. The three phase motor is being modeled in the equivalent two phase motor (q−d) space, while the modeling of the faulty case is being also formulated. The motor is being identified by the utilization of Set Membership Identification (SMI) that has the merit of identifying both the parameters of the motor as also providing uncertainty safety bounds by calculating orthotopes which bounds the systems parameter vector. Based on the volume and the trend of these orthotopes, rules for identifying the existence of a fault are being presented. If the current valuesof the identified parameters do not lie inside the safety bounds in the healthy case, but lie in an area that is being defined by the model of the short circuit case, then a fault is being triggered. Detailed analysis of the proposed approach as also extended simulation results are being presented that prove the efficiency of the suggested scheme.

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