International Standard Serial Number:

ISSN 1001-4551


Zhejiang University;

Zhejiang Machinery and Electrical Group

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Editorial of Journal of Mechanical & Electrical Engineering

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LUO Xiang-yang






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Fault diagnosis method of fan audio signal based on multi-scale feature extraction
Published:2023-03-23 author:SUN Qi-tao, LUO Zhi-sun, LIANG Hao, et al. Browse: 317 Check PDF documents
Fault diagnosis method of fan audio signal based 
on multi-scale feature extraction

SUN Qi-tao, LUO Zhi-sun, LIANG Hao, LU Na-na

(Ming Yang Smart Energy Group Limited, Zhongshan 528436, China)

Abstract: Aiming at the problem that the audio signal of fan drive chain components was complicated and fault identification was difficult to realize with a single feature extraction method, a fault diagnosis method for audio signals based on multiscale feature extraction was proposed. Firstly,the collected audio files of fan drive chain components were used to convert to digital signals and preprocess the data.Secondly, multi-scale feature extraction was performed on the audio signal. Five major features were extracted from the three dimensions of time domain, frequency domain and cepstral domain to form a multi-dimensional composite feature matrix. Then, the features were analyzed and the dimension of the feature matrix was reduced. Finally, the support vector machine (SVM) classification predictor was used to perform supervised learning on the multi-dimensional composite feature matrix, and the particle swarm optimization (PSO)was used to optimize the SVM parameter selection process. Through multiple sets of comparative experiments, the performance of PSO-SVM classification predictor on audio signal pattern classification was verified. The experimental results indicate that the extracted multiscale features can well represent the information of the audio signal, and have certain robustness.The SVM classification model optimized by PSO eliminates the blindness of parameter selection, which can achieve more than 98%accuracy rate of audio signal pattern recognition for fan drive chain components,and has good generalization.

Key words:  drive chain components; multi-dimensional composite feature matrix; feature extraction method; support vector machine(SVM); particle swarm optimization(PSO); fault classification
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