In:
Journal of Vibroengineering, JVE International Ltd., Vol. 19, No. 8 ( 2017-12-31), p. 5947-5959
Kurzfassung:
Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions.
Materialart:
Online-Ressource
ISSN:
1392-8716
,
2538-8460
DOI:
10.21595/jve.2017.18413
Sprache:
Englisch
Verlag:
JVE International Ltd.
Publikationsdatum:
2017
ZDB Id:
2942992-4
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