Szczegóły publikacji
Opis bibliograficzny
Supervised classification methods in condition monitoring of rolling element bearings / Paweł Różak, Jakub Zieliński, Piotr CZOP, Adam JABŁOŃSKI, Tomasz BARSZCZ, Michał Mareczek // W: Advances in condition monitoring of machinery in non-stationary operations : proceedings of the 5th international conference on Condition Monitoring of Machinery in Non-stationary Operations, CMMNO'2016, 12–16 September 2016, Gliwice, Poland / eds. Anna Timofiejczuk, [et al.]. — Cham : Springer International Publishing, cop. 2018. — (Applied Condition Monitoring ; ISSN 2363-698X ; vol. 9). — ISBN: 978-3-319-61926-2; e-ISBN: 978-3-319-61927-9. — S. 133–145. — Bibliogr. s. 144–145, Abstr. — Publikacja dostępna online od: 2017-09-22
Autorzy (6)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 112287 |
|---|---|
| Data dodania do BaDAP | 2018-02-28 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-319-61927-9_13 |
| Rok publikacji | 2018 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 5th international conference on Condition Monitoring of Machinery in Non-stationary Operations |
| Czasopismo/seria | Applied Condition Monitoring |
Abstract
Operational vibrational diagnostics is crucial for providing the reliability of mid and large scale combustion engine applications (e.g. railway, automotive heavy vehicles or electric generators). This work reports study presenting application of supervised learning and classification methods based on pattern recognition using different classifiers (e.g. logistic regression, k-nearest neighbor or normal density) in order to detect early warning diagnostic symptoms of malfunctioned rolling element bearings (REBs) in the presence of background disturbances from combustion diesel engine. The REB’s malfunction type classification is based on time domain (RMS, peak to peak, Crest factor) as well as frequency domain signal processing methods like envelope analysis or modulation intensity distribution (MID) which allows to neglect the influence of background noise representing by non-stationary operating conditions and possible structural modifications (e.g. maintenance activities or parts replacing). The proposed data classification methods are compared and validated by using experimental measurements conducted on a dedicated combustion engine test bench for wide range of rotational speed and different levels of REB’s radial load.
