Modern Manufacturing Engineering ›› 2018, Vol. 458 ›› Issue (11): 155-161.doi: 10.16731/j.cnki.1671-3133.2018.11.027

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Review of sparse decomposition methods and their applications in fault diagnosis of rotating machinery

Hu Nianwei1,2, Yang Jianwei1,2, Yao Dechen1,2   

  1. 1 School of Machine-electricity and Automobile Engineering,Beijing University of Civil Engineering Architecture,Beijing 100044,China;
    2 Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles,Beijing University of Civil Engineering Architecture,Beijing 100044,China
  • Received:2018-02-05 Published:2019-01-07

Abstract: In the recent two decades,especially in the last ten years,the theory and application of sparse decomposition have made great progress,and a great deal of literature has also been published in the field of fault diagnosis.Based on the published literature,the development of sparse decomposition is reviewed.Based on the brief introduction of sparse decomposition method,the existing improved algorithms are summarized according to the existing problems,including improving the atomic library and improving the computing speed.The fast algorithm mainly introduces the intelligent sparse decomposition method.The application of sparse decomposition in rotating machinery fault diagnosis is summarized.Some new research trends are also discussed,including artificial intelligence sparse decomposition,sparse decomposition for specific signal and compound fault sparse decomposition method which is helpful to the development of fault diagnosis technology.It is concluded that the sparse decomposition method has a broad prospect in the development of rotating machinery fault diagnosis.In the future,a large number of new algorithms combining artificial intelligence will appear.

Key words: sparse decomposition, rotating machinery, fault diagnosis, artificial intelligence

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