Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220400128-8.doi: 10.11896/jsjkx.220400128

• Software & Interdiscipline • Previous Articles     Next Articles

Diesel Engine Fault Diagnosis Based on Kernel Robust Manifold Nonnegative Matrix Factorizationand Fusion Features

LIU Hongyi1,2, WANG Rui3, WU Guanfeng1,2, ZHANG Yang1,2   

  1. 1 School of Mathematics,Southwest Jiaotong University,Chengdu 611756,China;
    2 National-Local Joint Engineering Laboratory of System Credibility Automatic Verification,Chengdu 611756,China;
    3 China Academy of Aerospace Electronics Technology,Beijing 100094,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:LIU Hongyi,born in 1998,postgra-duate,is a member of China Computer Federation.His main research interests include machine learning and fault diagnosis. WU Guanfeng,born in 1986,Ph.D,is a member of China Computer Federation.His main research interests include intelligent information processing and parallel computing.
  • Supported by:
    National Natural Science Foundation of China(62106206).

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Abstract: The diesel engine is one of the important power sources in industrial production,its failure will cause a huge impact on the efficiency and safety of industrial production,it is of great significance to diagnose the fault of diesel engine.Aiming at the difficulty and low accuracy of feature extraction in diesel engine valve fault diagnosis,a diesel engine fault diagnosis method based on kernel robust manifold non-negative matrix factorization method and fusion feature is proposed.Firstly,the pressure signal is analyzed in the time domain to extract the pressure characteristics.Secondly,the time-frequency analysis of the vibration signal is carried out using the short-time flourier transform(STFT),and the features of the vibration signal are extracted by the kernel robust manifold nonnegative matrix factorization.Then the features of the pressure signal and vibration signal are fused.Finally,support vector machine is used to realize fault diagnosis.Compared with the traditional method,the fault diagnosis accuracy of this method can reach 100% on the collected data set,which proves that it can effectively extract features and significantly improve the diagnosis accuracy.

Key words: Diesel engine, Fault diagnosis, Nonnegative matrix factorization, Feature extraction, Fusion feature

CLC Number: 

  • TP206
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