JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 30-37.doi: 10.6040/j.issn.1672-3961.0.2017.193

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Early diagnosis and life prognosis for slowlyvarying fault based on deep learning

ZHOU Funa1, GAO Yulin1*, WANG Jiayu1, WEN Chenglin2   

  1. 1. School of Computer and Information Engineering, Henan University, Kaifeng 475004, Henan, China;
    2. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, Zhejiang, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

Abstract: In order to overcome the shortcoming of traditional early fault diagnosis methods, a method of combining deep learning with PCA to realize early diagnosis of slowly varying small fault and life prognosis was proposed. Using the deep learning method to extract the sampled data characteristics layer by layer, learning the early fault characteristics and establishing the early fault diagnosis model for slowly varying small fault, the combining deep learning was combined with PCA to integrate the high dimensional fault feature vector extracted by the deep learning into a fault characteristic variable. A data-driven fault precursor could be defined according to the evolution rule of the characteristic variable of the historical fault data, and life prognosis model was established by exponential nonlinear fitting method. The TE benchmark data was used to verify the effectiveness of the proposed algorithm, experimental results showed the validity of the proposed algorithm by comparing with other algorithms.

Key words: early diagnosis, life prognosis, slowly varying faults, nonlinear fitting, deep learning

CLC Number: 

  • TP277
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