Change search
Refine search result
1 - 24 of 24
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    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.

  • 2.
    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.

  • 3.
    Mustafa, Mohammed Obaid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Faults detection and diagnosis for three phase induction machines2012Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Three phase induction motors have been intensively utilized in industrial applications, mainly due to their efficiency and reliability. These motors have good properties such as: increased stability and robustness, durability, large power to weight ratio, low production costs and controllability easiness. The most common faults that could happen in the rotor and the stator are: a) short circuit in stator winding, b) broken rotor bars, c) bearing failures, and d) dynamic or static air gap irregularities. These types of faults, are necessary to be identified and categorized, as soon as possible as they can end up in serious damages if not detected in due time.The aim of this licentiate thesis is to present a model based fault detection and diagnosis schemes for three phase induction motors relying on the Set Membership Identification (SMI) approach. In the proposed scheme proper uncertainty bounds and boundary violation rules have been established for detecting and categorizing the fault occurrences. The novel presented diagnostic and fault detection methods are able to detect and classify two types of induction motor faults: a) broken rotor bars, and b) short circuit in stator winding. As it will be analytically presented in the thesis, during the initialization of the algorithm, the parameters of the healthy induction motor are being identified by the utilization of the Recursive Least Squares, extended by the Set Membership concepts, where corresponding uncertainty bounds are also being recursively being calculated based on the assumed noise levels. In the sequel the proposed bound violation conditions for the fault detection and fault diagnosis are being online evaluated, on the converged identified motor parameters, within a sliding time window.The simulation results have been presented motor performance in healthy and faulty cases such as stator currents, rotor currents, torque, and angular speed of the motor. The efficiency of the proposed scheme has been extended evaluated with simulation studies for the cases of: a) one broken bar fault, b) 2\% short circuit fault, c) multiple number of broken bars.

  • 4.
    Mustafa, Mohammed Obaid
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Fault Detection, Diagnosis and Monitoring for Induction Motors2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis, multiple methods and different approaches have been established and evaluated successfully, in order to detect and diagnose the faults of induction motors (IMs). The aim of this thesis is to present novel fault detection and isolation methods for the case of induction machines that would have the merit to be implemented online and being characterized by specific novel capabilities, when compared with the existing techniques. More specifically both the cases of model based and modeless (model free) fault detection and isolation methods will be considered. The proposed methods have been based on: a) Set Membership Identification, Uncertainty Bounds Violation and a minimum uncertainty boundary violation detection schemes, for multiple cases of broken bars under different load conditions and short circuits in stator windings detection having the merit of exact and fast fault detection an easy straight forward fault isolation and capabilities, b) model based Support Vector Classification for the detection of broken bars under full load conditions, using features based on the spectral analysis of the steady state stator's current, without the need of training steps (an expensive, time consuming and often practically infeasible task) and existing of a priori data sets of healthy and faulty cases, c) fault classification based on robust linear discrimination scheme in the model free case and based on novel extracted features for both short circuit and broken bar, and d) fault detection based on Principal Component Analysis (PCA) fault/anomaly detector in time domain for detecting broken rotor bars under full load conditions, e) fault classification technique for bearings based on a novel Minimum Volume Ellipsoid method for feature extraction. One of additional major contributions of this thesis is the fact that especially for the cases of broken bars, and short circuit in stator windings. All the proposed methodologies have been extensively evaluated in multiple experiments and in multiple payloads and thus it has been realistically demonstrated the merits of all the proposed fault detection and isolation schemes. Furthermore, the obtained results suggest that these novel representations can be used within condition monitoring systems.

  • 5.
    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.

  • 6.
    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.

  • 7.
    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.

  • 8.
    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.

  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    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.

  • 13.
    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.

  • 14.
    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.

  • 15.
    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.

  • 16.
    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.

  • 17.
    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.

  • 18.
    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.
    Uncertainty Bounds Violation Scheme for Fault Detection in Induction Motors: Application to Broken Rotor Bars2014Conference paper (Other academic)
  • 19.
    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.
    Uncertainty Bounds Violation Scheme for FaultDetection in Induction Motors: Application to Broken Rotor Bars2014Conference paper (Refereed)
  • 20.
    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.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Fault Detection Scheme Based on Minimum Uncertainty Bounds Violation for Broken Rotor Bars in Induction Motors2016In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 48, p. 63-77Article in journal (Refereed)
    Abstract [en]

    In this article, a novel method for broken bars fault detection in the case of three-phase induction motors and under different payloads will be presented and experimentally evaluated. In the presented approach, the cases of a partially or full broken rotor bars is being also considered, caused by: a) drilling 4mm and 8mm out of the $13$mm thickness of the same rotor bar, and b) fully drilled (13mm) one, two and three broken bars. The proposed fault detection method is based on the Set Membership Identification (SMI) technique and a novel proposed minimum boundary violation fault detection scheme, applied on the identified motor's parameters. The system identification procedure is being carried out on the simplified equivalent model of the induction motor, during the steady-state operation (non-fault case), while at the same time the proposed scheme is able to calculate on-line the corresponding safety bounds for the identified variables, based on a priori knowledge of the measuring corrupting noise (worst case encountered). The efficiency, the robustness and the overall performance of the established fault detection scheme is being extensively evaluated in multiple experimental studies and under various time instances of faults and load conditions.

  • 21.
    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.
    Saied, Basil
    Department of Electrical Engineering, University of Mosul.
    Broken bar fault detection based on set membership identification for three phase induction motors2012In: Proccedings of the 9th International Conference on Informatics in Control, Automation and Robotics: Rome, Italy, 28-31, July, 2012, SciTePress, 2012, p. 224-231Conference paper (Refereed)
    Abstract [en]

    This article presents a fault detection scheme for the case of a broken bar occurrence in a three phase induction motor. The proposed scheme relies on Set Membership Identification (SMI) and novel proposed boundary violation rules for the identified motor’s parameters. The model of the three phase induction motor is being transformed into an equivalent two phase model, described in the q−d space, for both the normal and the faulty case. 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 specific typeof a broken bar fault. The proposed conditions depend on: a) abnormal parameter jumps, and b) rapid changes in the volume of the bounding uncertainty, which is being formulated either by ellipsoids or orthotopes. Detailed analysis of the proposed approach as also extended simulation results are being presented that prove the efficiency of the proposed scheme.

  • 22.
    Mustafa, Mohammed Obaid
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Varagnolo, Damiano
    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.
    Detecting broken rotor bars in induction motors with model-based support vector classifiers2016In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 52, p. 15-23Article in journal (Refereed)
    Abstract [en]

    We propose a methodology for testing the sanity of motors when both healthy and faulty data are unavailable. More precisely, we consider a model-based Support Vector Classification (SVC) method for the detection of broken bars in three phase asynchronous motors at full load conditions, using features based on the spectral analysis of the stator's steady state current (more specifically, the amplitude of the lift sideband harmonic and the amplitude at fundamental frequency). We diverge from the mainstream focus on using SVCs trained from measured data, and instead derive a classifier that is constructed entirely using theoretical considerations. The advantage of this approach is that it does not need training steps (an expensive, time consuming and often practically infeasible task), i.e., operators are not required to have both healthy and faulty data from a system for checking it. We describe what are the theoretical properties and fundamental limitations of using model based SVC methodologies, provide conditions under which using SVC tests is statistically optimal, and present some experimental results to prove the effectiveness of the suggested scheme.

  • 23.
    Nikolakopoulos, George
    et al.
    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.
    Georgoulas, George
    Grekland.
    Project: Fault Detection in Bearings2013Other (Other (popular science, discussion, etc.))
  • 24.
    Nikolakopoulos, George
    et al.
    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.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Novikov, Evgeny
    Boliden, Sverige.
    Projekt: Feldetektering i elektriska maskiner2013Other (Other (popular science, discussion, etc.))
    Abstract [sv]

    Syftet med projektet är att utveckla och experimentellt utvärdera nya algoritmer för att upptäcka och isolera fel i elmotorer.

1 - 24 of 24
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf