Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (10): 1548-1555.doi: 10.23940/ijpe.20.10.p6.15481555

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Deep Learning in Fault Diagnosis of Complex Mechanical Equipment

Siyu Li, Shaoluo Huang*, Yangyang Zhang, Lijun Cao, and Weiyi Wu   

  1. Shijiazhuang Campus, Army Engineering University, Shijiazhuang, 050003, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:

Abstract: Deep learning is a branch of machine learning. It uses neural networks as a bridge to represent a large number of data. It is also one of the research directions of artificial intelligence. At present, it is widely used in computer vision, speech recognition, audio recognition, fault diagnosis, and other fields, and it has achieved good results. In view of deep learning in modern complex mechanical equipment, fault diagnosis, and health, health management plays an important role. In this paper, the convolution neural network method for equipment fault diagnosis is summarized based on the structural characteristics of modern large-scale mechanical equipment and the advantages of deep learning. With the help of hardware in the loop simulation simulator, starting from the process of fault mechanism modeling, fault simulation, data processing, and so on and compared with other neural networks in deep learning, the experiment shows that the method has high accuracy, which is of great significance for improving the efficiency of equipment fault diagnosis.

Key words: deep learning, fault diagnosis, neural networks, loop simulation simulator