Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu
{"title":"Machine fault diagnosis based on multi-head deep learning network","authors":"Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu","doi":"10.1117/12.2581262","DOIUrl":null,"url":null,"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%.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Signal Processing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2581262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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%.