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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
Add:
No.9 Gaoguannong,Daxue Road,Hangzhou,China
P.C:
310009
E-mail:
meem_contribute@163.com
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 multiscale 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 multiscale 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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- Chinese Core Periodicals
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- Chinese Sci-tech Core Periodicals
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- SA, INSPEC Indexed
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- CSA: T Indexed
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- UPD:Indexed
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2010 Zhejiang Information Institute of Mechinery Industry
Technical Support：Hangzhou Bory science and technology
