Study on a Novel Fault Diagnosis Method Based on VMD and BLM

• Main Authors: Jianjie Zheng, Yu Yuan, Li Zou, Wu Deng, Chen Guo, Huimin Zhao
• Format: Article
• Language: English
• Published: MDPI AG 2019-06-01
• Series: Symmetry
• Subjects: rolling bearings; fault diagnosis; broad learning model; variational mode decomposition; Hilbert transform
• Online Access: https://www.mdpi.com/2073-8994/11/6/747

The bearing system of an alternating current (AC) motor is a nonlinear dynamics system. The working state of rolling bearings directly determines whether the machine is in reliable operation. Therefore, it is very meaningful to study the fault diagnosis and prediction of rolling bearings. In this paper, a new fault diagnosis method based on variational mode decomposition (VMD), Hilbert transform (HT), and broad learning model (BLM), called VHBLFD is proposed for rolling bearings. In the VHBLFD method, the VMD is used to decompose the vibration signals to obtain intrinsic mode functions (IMFs). The HT is used to process the IMFs to obtain Hilbert envelope spectra, which are transformed into the mapped features and the enhancement nodes of BLM according to the complexity of the modeling tasks, and the nonlinear transformation mean according to the characteristics of input data. The BLM is used to classify faults of the rolling bearings of the AC motor. Next, the pseudo-inverse operation is used to obtain the fault diagnosis results. Finally, the VHBLFD is validated by actual vibration data. The experiment results show that the BLM can quickly and accurately be trained. The VHBLFD method can achieve higher identification accuracy for multi-states of rolling bearings and takes on fast operation speed and strong generalization ability.