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Online fault diagnosis method of rolling bearings with complex working conditions based on FSDD and MAC
Published:2023-03-23 author:SUN Wan-feng, WANG Yu, SUN Yu, et al. Browse: 486 Check PDF documents
Online fault diagnosis method of rolling bearings with complex 
working conditions based on FSDD and MAC

SUN Wan-feng, WANG Yu, SUN Yu, WU Kai

(School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract: The core parts of Pellet Mill need to run continuously for a long time under high temperature and high humidity conditions, which were prone to rolling bearing failures, which seriously affected production safety. Aiming at the problem that rolling bearing faults in the Pellet Mill under complex working conditions cannot be diagnosed online, a rolling bearing online fault diagnosis method based on the frequency and spatial domain decomposition (FSDD) and modal assurance criterion (MAC) was proposed. Firstly the vibration signal was measured online to collect vibration data from Pellet Mill under different working conditions. And the working conditions of Pellet Mill with the most severe vibration conditions were identified by the root mean square (RMS) value of the vibration signal of Pellet Mill under different work conditions. Then, the modal parameters, including frequencies, modal shapes, and damping ratios, were identified by frequency and spatial domain decomposition. And the modal assurance criterion was used to extract the fault characteristic frequency from the identified modal parameters, so the purpose of damage determination was achieved. Finally, the fault diagnosis of a faulty Pellet Mill, whose model is K15, was carried out as an example. The research results show that the fault characteristic frequency of the Pellet Mill is 57.83Hz, and the fault point is the outer ring of the bearing SKF 24024CC/W33. The proposed method can effectively identify the faults of rolling bearing under complex working conditions.

Key words:  bearing fault diagnosis; frequency and spatial domain decomposition(FSDD); modal assurance criterion(MAC); fault online identification; root mean square(RMS); modal parameters
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