《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

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Research of rotating machinery fault diagnosis based on PSO-PNN and CV-SVM

Published:2023-10-30 author:GONG Yongkang, LI Wen, YU Feifei, et al. Browse: 287 Check PDF documents

Research of rotating machinery fault diagnosis based on

PSO-PNN and CV-SVM

GONG Yongkang 1, LI Wen 2, YU Feifei3, DU Canyi1, CHEN Guoyan3, LIU Liwu1

(1.School of Automotive and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China;

2.School

of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China;

3.School of Mechatronic

Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

Abstract: Different types of rotating machinery faults excited vibration signals with different characteristics. Aiming at the problem that it was difficult to judge the fault point of rotating machinery and the compound fault was not accurate,two kinds of artificial intelligence models, probabilistic neural network (PNN) and support vector machine (SVM) were constructed to identify rotating machinery faults. Firstly, the vibration signals of the research object under each fault state were collected, and the time domain and frequency spectrum of the vibration signals were analyzed. According to the characteristic performance of the vibration signals, the original vibration signal amplitude and vibration signal characteristic value were respectively used as the input vector of the artificial intelligence model. Then, the particle swarm optimization (PSO) was used to optimize the input parameters of the probabilistic neural network, and the cross validation (CV) was used to optimize the input parameters of the support vector machine. Finally, the probabilistic neural network and support vector machine fault diagnosis model were established to diagnose rotating machinery faults, and the diagnosis results were compared and analyzed. The results show that the accuracy of rotating machinery fault identification based on PSO-PNN model is more than 97%. The accuracy of fault identification of rotating machinery based on CV-SVM model is more than 98%. These two artificial intelligence methods have the advantages of fast speed and high accuracy in the fault diagnosis of rotating machinery. PSO-PNN method is suitable for realtime monitoring of rotating machinery faults, and CV-SVM method is suitable for identifying complex rotating machinery faults.

Key words: rotating machinery; particle swarm optimization(PSO); probabilistic neural network(PNN); cross validation(CV); support vector machine (SVM); accuracy of fault identification

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