Machine fault diagnosis based on multi-head deep learning network

Qidong Lu; Yu Qin; Yingying Li; Zhiliang Qin; Xiaowei Liu

doi:10.1117/12.2581262

20 January 2021 Machine fault diagnosis based on multi-head deep learning network

Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu

Author Affiliations +

Proceedings Volume 11719, Twelfth International Conference on Signal Processing Systems; 117190V (2021) https://doi.org/10.1117/12.2581262
Event: Twelfth International Conference on Signal Processing Systems, 2020, Shanghai, China

Abstract

Feature extraction and utilization is of great importance for the problem of machine fault diagnosis. In this paper, multihead deep learning network is proposed to achieve machine health status classification using features of different sizes. Firstly, statistical characteristics which reflect machine signal status of time domain and frequency domain are summarized to compose feature vectors as one-dimensional network input. Secondly, Mel power spectrum and its incremental characteristics are utilized as two-dimensional network input of three channels. Lastly, the multi-head network is introduced to analyze both one-dimensional and two-dimensional features using two different sub neural networks and classify the machine health status according to the joint feature analyzing result. The experiments on bearing working status database of Case Western Reserve University show that the proposed method has good mechanical signal classification ability and better stability. Moreover, our final test accuracy of fault diagnosis on 16 kinds of bearing working signals can reach up to about 99.53%.

Citation Download Citation

Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, and Xiaowei Liu "Machine fault diagnosis based on multi-head deep learning network", Proc. SPIE 11719, Twelfth International Conference on Signal Processing Systems, 117190V (20 January 2021); https://doi.org/10.1117/12.2581262

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KEYWORDS

Feature extraction

Convolutional neural networks

Data modeling

Signal processing

Detection and tracking algorithms

Statistical modeling

Neural networks

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