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  • 1.
    Al-Douri, Yamur
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Two-Level Multi-Objective Genetic Algorithm for Risk-Based Life Cycle Cost Analysis2019Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Artificial intelligence (AI) is one of the fields in science and engineering and encompasses a wide variety of subfields, ranging from general areas (learning and perception) to specific topics, such as mathematical theorems. AI and, specifically, multi-objective genetic algorithms (MOGAs) for risk-based life cycle cost (LCC) analysis should be performed to estimate the optimal replacement time of tunnel fan systems, with a view towards reducing the ownership cost and the risk cost and increasing company profitability from an economic point of view. MOGA can create systems that are capable of solving problems that AI and LCC analyses cannot accomplish alone.

    The purpose of this thesis is to develop a two-level MOGA method for optimizing the replacement time of reparable system. MOGA should be useful for machinery in general and specifically for reparable system. This objective will be achieved by developing a system that includes a smart combination of techniques by integrating MOGA to yield the optimized replacement time. Another measure to achieve this purpose is implementing MOGA in clustering and imputing missing data to obtain cost data, which could help to provide proper data to forecast cost data for optimization and to identify the optimal replacement time.

    In the first stage, a two-level MOGA is proposed to optimize clustering to reduce and impute missing cost data. Level one uses a MOGA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Level two uses MOGA to impute the missing cost data by using a valid data period from that are reduced data in size. In the second stage, a two-level MOGA is proposed to optimize time series forecasting. Level one implements MOGA based on either an autoregressive integrated moving average (ARIMA) model or a dynamic regression (DR) model. Level two utilizes a MOGA based on different forecasting error rates to identify proper forecasting. These models are applied to simulated data for evaluation since there is no control of the influenced parameters in all of the real cost data. In the final stage, a two-level MOGA is employed to optimize risk-based LCC analysis to find the optimal replacement time for reparable system. Level one uses a MOGA based on a risk model to provide a variation of risk percentages, while level two uses a MOGA based on an LCC model to estimate the optimal reparable system replacement time.

    The results of the first stage show the best cluster centre optimization for data clustering with low  and high intensity. Three cluster centres were selected because these centres have a geometry that is suitable for the highest data reduction of 27%. The best optimized interval is used for imputing missing data. The results of the second stage show the drawbacks of time series forecasting using a MOGA based on the DR model. The MOGA based on the ARIMA model yields better forecasting results. The results of the final stage show the drawbacks of the MOGA based on a risk-based LCC model regarding its estimation. However, the risk-based LCC model offers the possibility of optimizing the replacement schedule.

    However, MOGA is highly promising for allowing optimization compared with other methods that were investigated in the present thesis.

  • 2.
    Al-Douri, Yamur
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hamodi, Hussan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data imputing using genetic algorithms (GA): A case study of cost data for tunnel fans2017Konferensbidrag (Refereegranskat)
  • 3.
    Al-Douri, Yamur K.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Information assurance for maintenance of railway track2016Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Railway traffic is steadily increasing, having a negative impact on maintenance and resulting in decreased track availability, comfort, and safety. Swedish railway track maintenance mostly focuses on the actual track condition via a nationwide condition-based maintenance (CBM) strategy. For maintenance to be conducted in an appropriate way, data on the actual track condition must be accurate; furthermore, those data need to be converted into accurate information for maintenance decisions. An information assurance (IA) framework has the potential to deal with the system risks from a technical perspective. The framework is a guideline that can be implemented within CBM to understand both condition monitoring data behaviour and the information processing used to reach maintenance decisions.This research investigates ways of an information assurance (IA) framework can be implemented in the following CBM steps: data collecting, data processing and making maintenance decisions on Swedish railway. The framework can be used to understand data behaviour, information processing and the communication between information layers for decisions at organisation, infrastructure and data/information levels. The research uses both qualitative and quantitative methods to investigate critical information data, parameters, and problems and to suggest which areas need improvement. Quantitative analysis of the Swedish track geometry database reveals specific information about the behaviour of the railway data and their processing to make maintenance decisions.A case study shows how certain sections of a railway track are monitored and evaluates maintenance practices on those sections. The study finds several different types of measurements are taken using several different measurement systems. It is difficult to integrate these data for proper processing. In addition, there are problems of incomplete or irregular data; this affects the derivation of information and the use of models to understand track irregularities.Given the problems of data processing and subsequent decision making, the study suggests implementing an IA framework with CBM. The study checks the achievement of three IA principles in the existing data: authenticity, integrity and availability. The results show data have problems of authenticity and integrity, something also mentioned by the stakeholders in interviews. In particular years and on certain track sections, CM data are more than 5 percent incomplete, significantly affecting analysis. Incomplete track measurement data reach as high as 63 percent for the parameters of standard deviation (STD), longitudinal level and STD cooperation. Inaccurate measured values for alignment long wavelength within certain speed limits reach as high as 71 percent. These indicators are important for calculating track quality but are either incomplete or incorrect, negatively affecting the calculation of the Q-value and estimations of the track quality. This, in turn, negatively affects the maintenance decisions. Using information assurance will increase the system performance by permitting stakeholders to make accurate decisions.The suggested information assurance framework can discover technical problems but it needs to be improved using technologies, techniques and services to ensure complete and accurate data are available to be processed for maintenance decisions.

  • 4.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Al-Jumaili, Mustafa
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Information security in e-maintenance: a study of Scada security2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    eMaintenance solutions are spreading increasingly due to the continuous evolution in the different Information and Communication Technology (ICT) tools. In general, most of the available eMaintenance solutions are depending on Internet infrastructure what makes them vulnerable to all security threats that affect the Internet. One of the important eMaintenance solutions is Supervisory Control and Data Acquisition (SCADA) system as it has been used in most of the industrial processes. SCADA systems were designed without security considerations as they were mainly installed into isolated networks. Nowadays, SCADA systems are mainly connected to Internet and other networks. Therefore, SCADA systems have been exposed to wide range of network security threats. Hence, SCADA security has become an important aspect that needs to be investigated. In this paper, a study of SCADA security issues will be done. The main contribution of this paper is to address SCADA security issues and challenges related to eMaintenance.

  • 5.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hamodi, Hussan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Data imputing using generic algorithms (GA)2017Ingår i: Mine Planning and Equipment Selection (MPES 2017): Proceeding of the 26th International Symposium on Mine Planning and Equipment Selection Luleå, Sweden, August 29-31, 2017 / [ed] Behzad Ghodrati, Uday Kumar, Håkan Schunnesson, Luleå: Luleå tekniska universitet, 2017, s. 205-208Konferensbidrag (Refereegranskat)
  • 6.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hamodi, Hussan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Lundberg, Jan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Time Series Forecasting using a Two-level Multi-objective Genetic Algorithm: A case study of cost data for tunnel fans2018Ingår i: Algorithms, ISSN 1999-4893, Vol. 11, nr 8, artikel-id 123Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The aim of this study is to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level is for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements either a multi-objective GA based on the ARIMA model or based on the dynamic regression model. The second level utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with the ARIMA model only. The results show the drawbacks of time series forecasting using the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In the second level, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

  • 7.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Hamodi, Hussan
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Zhang, Liangwei
    Department of Industrial Engineering, School of Mechanical Engineering, Dongguan University of Technology, 523808 Dongguan, China.
    Data clustering and imputing using a two-level multi-objective genetic algorithms (GA): A case study of maintenance cost data for tunnel fans2018Ingår i: Cogent Engineering, ISSN 2331-1916, Vol. 5, nr 1, s. 1-16, artikel-id 1513304Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Data clustering captures natural structures in data consisting of a set of objects and groups similar data together. The derived clusters can be used for scale analysis and to posit missing data values in objects, as missing data have a negative effect on the computational validity of models. This study develops a new two-level multi-objective genetic algorithm (GA) to optimize clustering in order to redact and impute missing cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level uses a multi-objective GA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Our clustering model is validated using k-means clustering. The second level uses a multi-objective GA to impute the missing cost redacted data in size using a valid data period. The optimal population has a low , 0.1%, and a high intensity, 99%. It has three cluster centres, with the highest data reduction of 27%. These three cluster centres have a suitable geometry, so the cost data can be partitioned into relevant contents to be redacted for imputing. Our model show better clustering detection and evaluation compared with k-means. The amount of missing data for the two cost objects are: labour 57%, materials 81%. The second level shows highly correlated data (R-squared 0.99) after imputing the missing data objects. Therefore, multi-objective GA can cluster and impute data to derive complete data that can be used for better estimation of forecasting.

  • 8.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Parida, Aditya
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kumar, Uday
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Model-based security system for data acquisition in e-maintenance using artificial immune system and cloud computing2012Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 15, nr 4, s. 26-37Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    eMaintenance solutions are extensively used by the industry today. eMaintenance is an emerging technology aimed to support the industry to achieve effectiveness and efficiency in their maintenance process through enhanced use of Information and Communication Technology (ICT) . One of the essential components is an eMaintenance solution is data acquisition. Supervisory Control and Data Acquisition (SCADA) has been used to manage data acquisition is many industrial systems. Nowadays, modern SCADA systems are available through internet and other networks via IP protocol. An increased use of internet–based solution requires appropriate management approaches to improve the safety and security aspects of a system. Hence, this paper suggests a new security model based security for SCADA systems through Cloud computing and Artificial Immune System (AIS). Furthermore, the paper provides AIS, which is based on Decision Tree (C4.5 algorithm) using clustered feature set. The features set are selected from NSL-KDD cup. It is a new version of KDD dataset. As a result, two Antibodies are generated (that could recognize Normal and Antigen). After applying the resulted antibodies on the testing data set, the outputs are Normal, Antigen, and Unknown. Finally it is treated with Unknown as Antigen. As a result, high accuracy of the suggested model reaches 96.3%.

  • 9.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Pangracious, Vinod
    Department of Electrical and Computer Engineering, American University in Dubai,.
    Al-Doori, Mulhim
    Department of Electrical and Computer Engineering, American University in Dubai.
    Artifical Immune System Using Genetic Algorithm And Decision Tree2016Ingår i: International Conference on Bio-engineering for Smart Technologies (BioSMART) 2016, Piscataway, NJ: IEEE, 2016, s. 1-4, artikel-id 7835603Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial immune system (AIS) is considered as an adaptive computational intelligence method that could be used for detecting and preventing current computer network threats. AIS generates Antibodies (self) competent in recognizing Antigen (non-self), which is considered as an anomaly technique. This paper aims to develop artificial immune system (AIS) that consists of two levels. Level one is developed using Genetic Algorithm, while level two is developed using C4.5 decision tree algorithm. The proposed system trained with clustered features that are selected from NSL-KDD cup data-set. Each level produces two antibodies (that could recognize Normal and Antigen access-records). The recognition accuracy of the developed system reaches 96%. The behavior of each level is studied. The best feature-set that suits each level is specified.

  • 10.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Tretten, Phillip
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    A critical review of Information Assurance (IA) framework forcondition-based maintenance of railway tracks2017Ingår i: Risk, Reliability and Safety: Innovating Theory and Practice - Proceedings of the 26th European Safety and Reliability Conference, ESREL 2016 / [ed] Walls L.,Revie M.,Bedford T, London: CRC Press, 2017, s. 1072-1078Konferensbidrag (Refereegranskat)
    Abstract [en]

    Railway maintenance is faced with increasing demands, including the need to improve service.Data measuring the track state and suitable models or applications are needed to make good maintenancedecisions. This critical review paper investigates many research papers on the use of information assurance (IA)within condition-based maintenance (CBM) on a railway track. An IA framework sheds light on the data andinformation used to make maintenance decisions. The paper considers work on data processing and decisionmakingin CBM. The results show condition monitoring suffers from an inability to determine exact positioningon the track; some data are inaccurate or unavailable. Existing studies have not adequately dealt with data contentor the various technologies used. They focus on integrity, availability, authentication, authorisation and accuracy,but do not consider other IA principles important to understand data.CBMmodels and algorithms have difficultyunderstanding degradation models, and data problems mean it is difficult to make good decisions. There is alack of long term maintenance plans. Models also need to be integrated for more realistic but not necessarilyoptimum solutions and to ensure practical predictions of maintenance. Some models focus on degradation, othersconsider prediction, and still others calculate the maintenance cost; it is difficult to combine these. Overall, dataare inaccurate, there is no testing phase using realistic data, and existing models are insufficient. This has anegative impact on maintenance decisions.

  • 11.
    Al-Douri, Yamur K.
    et al.
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Tretten, Phillip
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karim, Ramin
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Improvement of Railway Performance: A Study of Swedish Railway Infrastructure2016Ingår i: Journal of Modern Transportation, ISSN 2095-087X, E-ISSN 2196-0577, Vol. 24, nr 1, s. 22-37, artikel-id 2Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The volume of rail traffic was increased by 5% from 2006 to 2010, in Sweden, due to increased goods and passenger traffic. This increased traffic, in turn, has led to a more rapid degradation of the railway track, which has resulted in higher maintenance costs. In general, degradation affects comfort, safety, and track quality, as well as, reliability, availability, speed, and overall railway performance. This case study investigated the needs of railway stakeholders responsible for analysing the track state and what information is necessary to make good maintenance decisions. The goal is to improve the railway track performance by ensuring increased availability, reliability, and safety, along with a decreased maintenance cost. Interviews of eight experts were undertaken to learn of general areas in need of improvement, and a quantitative analysis of condition monitoring data was conducted to find more specific information. The results show that by implementing a long-term maintenance strategy and by conducting preventive maintenance actions maintenance costs would be reduced. In addition to that, problems with measured data, missing data, and incorrect location data resulted in increased and unnecessary maintenance tasks. The conclusions show that proactive solutions are needed to reach the desired goals of improved safety, improved availability and improved reliability. This also includes the development of a visualisation tool and a life cycle cost model for maintenance strategies.

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