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- W2781461570 abstract "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." @default.
- W2781461570 created "2018-01-05" @default.
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- W2781461570 date "2017-12-31" @default.
- W2781461570 modified "2023-10-12" @default.
- W2781461570 title "Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions" @default.
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- W2781461570 doi "https://doi.org/10.21595/jve.2017.18413" @default.
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