Gear classification and fault detection using a diffusion map framework
Sipola, T., Ristaniemi, T., & Averbuch, A. (2015). Gear classification and fault detection using a diffusion map framework. Pattern Recognition Letters, 53(1 February 2015), 53-61. https://doi.org/10.1016/j.patrec.2014.10.019
Julkaistu sarjassa
Pattern Recognition LettersPäivämäärä
2015Tekijänoikeudet
© 2015 Elsevier
This article proposes a system health monitoring approach that detects abnormal behavior of machines. Diffusion map is used to reduce the dimensionality of training data, which facilitates the classification of newly arriving measurements. The new measurements are handled with Nyström extension. The method is trained and tested with real gear monitoring data from several windmill parks. A machine health index is proposed, showing that data recordings can be classified as working or failing using dimensionality reduction and warning levels in the low dimensional space. The proposed approach can be used with any system that produces high-dimensional measurement data.
Julkaisija
Elsevier BV * North-Holland; International Association for Pattern RecognitionISSN Hae Julkaisufoorumista
0167-8655Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/24029048
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Gear classification and fault detection using a diffusion map framework
Sipola, Tuomo; Ristaniemi, Tapani; Averbuch, Amir (University of Jyväskylä, 2013) -
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