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Tomas Olsson has been working as a researcher and software developer at SICS Swedish ICT since 1998 and he has been a PhD student at Mälardalen University since 2011. He has a long experience in both software development and in data analysis, and his research interests are in statistical machine learning and big data analytics. Tomas has a MSc in Computer Science from KTH (1998) and a licentiate degree from Uppsala University (2006).
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 datadriven 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.
Main advisor: Peter Funk, Mälardalen University
Associate Professor Ning Xiong, Mälardalen University,
Dr. Marcus Bengtsson, Mälardalen University & Volvo Construction Equipment,
Professor Anders Holst, SICS Swedish ICT & KTH Royal Institute of Technology
Faculty examiner: Associate Professor Amy Loutfi, Örebro universitet
Professor Henrik Boström, Stockholm University,
Professor Maria Lindén, Mälardalen University,
Professor Niklas Lavesson, Blekinge Institute of Technology