Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

• Main Authors: Hongtao Xue, Man Wang, Zhongxing Li, Peng Chen
• Format: Article
• Language: English
• Published: JVE International 2017-12-01
• Series: Journal of Vibroengineering
• Subjects: sequential fault detection; sealed deep groove ball bearing; soft margin SVM; optimal composition of symptom parameter
• Online Access: https://www.jvejournals.com/article/18413

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.