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Fault diagnosis method of sound signal based on EMATE and POA-ELM
Published:2024-06-27 author:XU Zhejun, WANG Kai, LUO Shaojie, et al. Browse: 501 Check PDF documents
Fault diagnosis method of sound signal based on EMATE and POAELM


XU Zhejun1,2, WANG Kai3, LUO Shaojie4, CUI Bingrong5


(1.School of Computing, Chongqing University, Chongqing 400030, China; 2.School of Artificial Intelligence, Zhejiang Post and 

Telecommunication College, Shaoxing 312366, China; 3.Equipment Management Department, State Grid Zhejiang Electric Power 

Co., Ltd., Hangzhou 310063, China; 4.Technology Digitalization Department, State Grid Hangzhou Yuhang District Power Supply 

Company, Hangzhou 311199, China; 5.Digital Chip Design Center, Beijing Zhixin Microelectronics Technology Co., Ltd., 

Beijing 100192, China)


Abstract: The conventional fault diagnosis method of engineering machinery generally analyzes the vibration signal, but the vibration sensor needs to contact with the engineering machinery when the vibration signal is collected. In some cases, the engineering machinery surface is not suitable for installing the sensor, such as the temperature of the equipment is high or the installation space of the sensor is limited. To solve these problems, a fault diagnosis method for the engineering machinery based on the enhanced multiscale attention entropy (EMATE) and the pelican optimization algorithm optimized extreme learning machine (POA-ELM) was proposed, and the sound signal was taken as the fault diagnosis object. Firstly, the sound sensor was used to collect the sound signals of different faults of the engineering machinery to avoid the contact acquisition defects existing in the vibration sensor. Then, EMATE was used to extract fault information from the sound signal and establish feature vectors that represent different fault states of engineering machinery. Subsequently, considering the issue of optimizing the parameters of ELM, POA was used to optimize the key parameters of ELM, and an ELM classification model with adaptive parameter settings was established. Finally, the POA-ELM classifier was used to identify fault features and achieve fault identification of engineering machinery. The effectiveness of EMATE-POA-ELM based fault diagnosis methods was validated using the sound signal dataset of reciprocating compressor and rolling bearing. The research results indicate that using the EMATE method as a fault feature extraction indicator can respectively achieve a recognition accuracy of 100 % and 99.23%, and the feature extraction time is only 53.88 s and 172.47 s. Comparing with indicators such as multi-scale attention entropy, composite multi-scale attention entropy, and time-shifted multi-scale attention entropy, EMATE has higher average fault recognition accuracy and better comprehensive performance.

Key words: engineering machinery; reciprocating compressor; rolling bearing; fault data set; enhanced multiscale attention entropy (EMATE); fault diagnosis; pelican optimization algorithm optimized extreme learning machine (POA-ELM)


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