Bearing Fault Diagnosis using Deep Belief Networks

  • Xiang Ping Xiao
  • Tian Ran Lin
  • Kun Yu
Keywords: Rolling element bearing, Fault diagnosis, Genetic algorithm, Deep belief network


This paper presents an experimental study on bearing fault diagnosis using a Deep Belief Network and the Genetic Algorithm for parameter optimization. A bearing test-rig is proposedly built to simulate various bearing operation conditions in the study, namely, Healthy, Inner Race fault, Ball fault and Outer Race fault. The diagnosis technique is then employed to analyse the experimental data acquired from the bearing test-rig and to recognize the bearing operation conditions based on the fault patterns detected by the algorithm. It is shown that the diagnosis technique proposed in this study can successfully discriminate the four bearing fault conditions with rather high accuracy and a good computational efficiency.