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  • 1.
    Aslanidou, Ioanna
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Zaccaria, Valentina
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Rahman, Moksadur
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Oostveen, Mark
    Micro Turbine Technology bv, Eindhoven, Netherlands.
    Olsson, Tomas
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. RISE SICS, Västerås, Sweden.
    Kyprianidis, Konstantinos
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Towards an Integrated Approach for Micro Gas Turbine Fleet Monitoring, Control and Diagnostics2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. Prediction of faults can change the maintenance model of a system from a fixed maintenance interval to a condition based maintenance interval, further decreasing the total cost of ownership of a system. Technologies developed for engine health monitoring and advanced diagnostic capabilities are generally developed for larger gas turbines, and generally focus on a single system; no solutions are publicly available for engine fleets. This paper presents a concept for fleet monitoring finely tuned to the specific needs of micro gas turbines. The proposed framework includes a physics-based model and a data-driven model with machine learning capabilities for predicting system behaviour, combined with a diagnostic tool for anomaly detection and classification. The integrated system will develop advanced diagnostics and condition monitoring for gas turbines with a power output under 100 kW.

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  • 2.
    Olsson, Tomas
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Data-Driven Approach to Remote Fault Diagnosis of Heavy-duty Machines2015Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Heavy-duty machines are equipment constructed for working under rough conditions and their design is meant to withstand heavy workloads. However, the last decades technical development in cheap electronically components have lead to an increase of electrical systems in traditionally mainly mechanical systems of heavy-duty machines. As the complexity of these machines increases, so does the complexity of detecting and diagnosing machine faults. However, the addition of new electrical systems, such as on-board computational power and telematics, makes it possible to add new sensors that measure signals relevant for fault detection and diagnosis, and to process signals on-board or off-board the machines.

    In this thesis, we address the diagnostic problem by investigating data-driven methods for remote diagnosis of heavy-duty machines, where a part of the analysis is performed on-board the machine (fault detection), while another part is performed off-board the machine (fault classification). We propose a diagnostic framework where we use a novel combination of methods for each step in the diagnosis. On-board the machine, we have used logistic regression as an anomaly detector to detect faults that will lead to a stream of individual cases classified as anomalous or not. Then, either on-board or off-board, we can use a probabilistic anomaly detector to identify whether the stream of cases is truly anomalous when we look at the stream of cases as a group. The anomalous group of cases is called a composite case. Thereafter, off-board the machine, each anomalous individual case is classified into a fault type using a case-based reasoning approach to fault diagnosis. In the final step, we fuse the individual classifications into a single aggregated classification for the composite case. In order to be able to assess the reliability of a diagnosis, we also propose a novel case-based approach to estimating the reliability of probabilistic predictions. It can, for instance, be used for assessing the confidence of the classification of a composite case given historical data of the predictive reliability.

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  • 3.
    Olsson, Tomas
    Swedish Institute of Computer Science (SICS), Sweden.
    Evaluating Machine Learning for Predicting Next-Day Hot Water Production of a Heat Pump2013Ingår i: IEEE International Conference on Power Engineering, Energy and Electrical Drives: 4th International Conference on Power Engineering, Energy and Electrical Drives, 2013, s. 1688-1693Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper describes an evaluation of five machine learning algorithms for predicting the domestic space and hot- water heating production for the next day. The evaluated algorithms were the k-nearest neighbour algorithm, linear regression, regression tree, decision table and support vector machine regres- sion. The hot water production was measured in the ME3Gas project, where data was collected from two Swedish households that use the same type of geothermal heat pumps for space heating and hot-water production. The evaluation consisted of four experiments where we compared the regression performance by varying the number of previous days and the number of time periods for each day as input features. In the experiments, the k-nearest neighbour algorithm, linear regression and support vector machine regression had the best performance.

  • 4.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-based reasoning combined with statistics for diagnostics and prognosis2012Ingår i: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 364, nr 1, s. Article number: 012061-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.

  • 5.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gillblad, D.
    SICS Swedish ICT, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Explaining probabilistic fault diagnosis and classification using case-based reasoning2014Ingår i: Case-Based Reasoning Research and Development: 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings, 2014, s. 360-374Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

  • 6.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gillblad, Daniel
    SICS Swedish ICT, Kista, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Case-Based Reasoning for Explaining Probabilistic Machine Learning2014Ingår i: International Journal of Computer Science & Information Technology (IJCSIT), ISSN 0975-4660, E-ISSN 0975-3826, Vol. 6, nr 2, s. 87-101Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.

  • 7.
    Olsson, Tomas
    et al.
    SICS Swedish ICT, Sweden.
    Holst, Anders
    SICS Swedish ICT, Sweden.
    A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications2015Ingår i: Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, Hollywood, Florida, United States: AAAI , 2015, s. 434-439Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

  • 8.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. SICS Swedish ICT, Sweden.
    Källström, Elisabeth
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Gillblad, Daniel
    SICS Swedish ICT, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lindström, John
    Luleå University of Technology, Luleå, Sweden.
    Håkansson, Lars
    Blekinge Institute of Technology, Sweden.
    Lundin, Joakim
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Larsson, Jonas
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches2014Ingår i: Proceedings of the ICCBR 2014 Workshops, 2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a generic approach to fault diagnosis of heavy duty machines that combines signal processing, statistics, machine learning, and case-based reasoning for on-board and off-board analysis. The used methods complement each other in that the on-board methods are fast and light-weight, while case-based reasoning is used off-board for fault diagnosis and for retrieving cases as support in manual decision mak- ing. Three major contributions are novel approaches to detecting clutch slippage, anomaly detection, and case-based diagnosis that is closely in- tegrated with the anomaly detection model. As example application, the proposed approach has been applied to diagnosing the root cause of clutch slippage in automatic transmissions. 

  • 9.
    Olsson, Tomas
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. SICS Swedish ICT, Kista, Sweden.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Källström, Elisabeth
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Holst, Anders
    SICS Swedish ICT, Kista, Sweden.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Fault Diagnosis via Fusion of Information from a Case Stream2015Ingår i: Case-Based Reasoning Research and Development. Proceeding of the the 23th International Conference on Case-Based Reasoning (ICCBR-2015), 2015, s. 275-289Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a novel approach to fault diagnosis applied to a stream of cases. The approach uses a combination of case-based reasoning and information fusion to do classification. The approach consists of two steps. First, we perform local anomaly detection on-board a machine to identify anomalous individual cases. Then, we monitor the stream of anomalous cases using a stream anomaly detector based on a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR classifier. Thereafter, the individual classifications are aggregated into a composite case with a single prediction using a information fusion method. We compare three information fusion approaches: simple majority vote, weighted majority vote and Dempster-Shafer fusion. As baseline for comparison, we use the classification of the last identified anomalous case in the window as the aggregated prediction.

  • 10.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Tomas
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-Based Reasoning Supports Fault Diagnosis Using Sensor Information2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

  • 11.
    Xiong, Ning
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Olsson, Tomas
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Funk, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik.
    Case-based reasoning supports fault diagnosis using sensor information2013Ingår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 16, nr 4, s. 25-30Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Fault diagnosis and prognosis of industrial equipment become increasingly important for improving the quality of manufacturing and reducing the cost for product testing. This paper advocates that computer-based diagnosis systems can be built based on sensor information and by using case-based reasoning methodology. The intelligent signal analysis methods are outlined in this context. We then explain how case-based reasoning can be applied to support diagnosis tasks and four application examples are given as illustration. Further, discussions are made on how CBR systems can be integrated with machine learning techniques to enhance its performance in practical scenarios.

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