Study on fault diagnosis method of splitter intelligent wheelchair motor based on wavelet and energy feature. (October 2021)

• Record Type: Journal Article
• Authors: Yang, Cheng; Zhang, Mengfei; Zhou, Bo
• Is Part Of: Noise & vibration worldwide. Volume 52:Number 9(2021)
• Page Start: 251
• Page End: 260
• Publication Date: 2021-10
• Subjects: Bearing vibration -- wavelet de-noising -- energy feature -- fault diagnosis; Noise control -- Periodicals; Damping (Mechanics) -- Periodicals; Soundproofing -- Periodicals; Damping (Mechanics); Noise control; Soundproofing; Periodicals; 620.205
• DOI: 10.1177/09574565211000451
• Languages: English
• ISSNs: 0957-4565

Abstract

As a key component of a split-type intelligent sports wheelchair for the disabled, the reliability of the motor is related to the personal safety of the wheelchair user and the accurate realization of the wheelchair's sports functions. This motor is actually just a rotating machine. In order to achieve detection and analysis of rotating machinery bearing vibration signal, a method based on wavelet and energy feature of rotating machinery fault diagnosis is introduced. This method applies wavelet to obtain de-noising and then uses wavelet packet energy feature extraction to diagnose faults effectively caused by rotating machinery such as rotor unbalance fault, rotor misalignment fault, and rotor-to-stator rub fault. Test results illustrate that when different faults occur to the bearing, it is viable to utilize pattern recognition to diagnose faults for the reason that discrepancies appear in sub-hand energy after wavelet packet decomposition. The main research conclusions of this paper are also directly applied to the fault diagnosis of such wheelchair motors.