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dc.contributor.authorKansanaho, Jarno
dc.date.accessioned2019-11-08T14:18:12Z
dc.date.available2019-11-08T14:18:12Z
dc.date.issued2019
dc.identifier.isbn978-951-39-7936-2
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/66287
dc.description.abstractThis thesis focuses on the research and development of the data-driven methods used to diagnose rolling element bearings (REBs) and evaluates the software architectural design of these data-driven methods. REBs are vulnerable components in machinery. Vibration-based condition monitoring is a very popular methodology for monitoring the health of REBs. This research started with the development of methods to analyze and detect incipient local faults of REBs using vibration measurements. The main goal was to find weak vibration signatures generated by local faults in REBs. As a result, a flexible simulator was developed to analyze the vibrations of bearing faults and to evaluate vibration analysis methods, and a spline wavelet-based algorithm were introduced for fault detection. An incipient bearing fault will become enlarged if a machine is run and the faulty bearing has not been replaced. The identification of different lifetime stages of wear evolution is part of the input data for bearing diagnostics and prognostics. A method to detect different lifetime stages of REBs according to their vibration signals was proposed based on an unsupervised learning method. The result of the unsupervised method was exploited in early fault detection utilizing supervised methods. It is important to estimate the severity of a fault, and size is probably the best proxy for severity. Estimating the fault size of defective REBs is one of the top challenges in bearing diagnostics, especially when vibration measurements are used to determine the state of health. A novel method for feature ranking to estimate fault sizes for REBs was presented. Black-box classifiers were applied to detect non-linear relations between features, and it was concluded that the best metrics for basic diagnostics are not necessarily the best qualities for fault size estimation. The final part of this research focuses on design at system-level. Software framework designs encapsulate fault detection and remaining useful life (RUL) estimation methods. As part of the tribotronic system, the object-oriented framework considers bearing applications and potentially extends them to other mechanical applications. Keywords: Rolling element bearing, Bearing diagnostics, Vibration analysis, Feature extraction, Machine learning, Tribological system, Software frameworken
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherJyväskylän yliopisto
dc.relation.ispartofseriesJYU dissertations
dc.relation.haspart<b>Artikkeli I:</b>Kansanaho, J., Saarinen, K., & Kärkkäinen, T. (2017). Flexible Simulator for the Vibration Analysis of Rolling Element Bearings. <i>International Journal of COMADEM, 20 (2), 17-22.</i> <a href="http://apscience.org/comadem/index.php/comadem/article/view/15"target="_blank"> apscience.org/comadem/index.php/comadem/article/view/15</a>
dc.relation.haspart<b>Artikkeli II:</b> Kansanaho, J., Saarinen, K., & Kärkkäinen, T. (2018). Spline Wavelet based Filtering for Denoising Vibration Signals Generated by Rolling Element Bearings. <i>International Journal of COMADEM, 21 (4), 25-30.</i> <a href="https://apscience.org/comadem/index.php/comadem/article/view/105"target="_blank"> apscience.org/comadem/index.php/comadem/article/view/105</a>
dc.relation.haspart<b>Artikkeli III:</b> Kansanaho, Jarno; Kärkkäinen, Tommi (2019). Hybrid vibration signal monitoring approach for rolling element bearings. In <i>ESANN 2019 : Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN, 49-54.</i> <a href="https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-90.pdf"target="_blank"> www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-90.pdf</a>
dc.relation.haspart<b>Artikkeli IV:</b> Jarno Kansanaho, Tommi Kärkkäinen, Pietro Borghesani, Wade A. Smith, Robert B. Randall, Zhongxiao Peng (2019). Feature ranking for fault size estima-tion of rolling element bearings. <i>Submitted.</i>
dc.relation.haspart<b>Artikkeli V:</b> Jarno Kansanaho and Tommi Kärkkäinen. Software framework for Tri-botronic system. <i>Preprint.</i> <a href="https://arxiv.org/abs/1910.13764"target="_blank"> arXiv:1910.13764, 2019</a>
dc.rightsIn Copyright
dc.subjectlaakerit
dc.subjectkunnonvalvonta
dc.subjectviat
dc.subjectvärähtelyt
dc.subjectsignaalianalyysi
dc.subjectalgoritmit
dc.subjectkoneoppiminen
dc.subjectsovelluskehykset
dc.subjecttribologia
dc.subjectrolling element bearing
dc.subjectbearing diagnostics
dc.subjectvibration analysis
dc.subjectfeature extraction
dc.subjectmachine learning
dc.subjecttribological system
dc.subjectsoftware framework
dc.titleData-Driven Methods for Diagnostics of Rolling Element Bearings
dc.typeDiss.
dc.identifier.urnURN:ISBN:978-951-39-7936-2
dc.relation.issn2489-9003
dc.rights.copyright© The Author & University of Jyväskylä
dc.rights.accesslevelopenAccess
dc.type.publicationdoctoralThesis
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.date.digitised


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