Computer Science ›› 2023, Vol. 50 ›› Issue (5): 93-102.doi: 10.11896/jsjkx.220500197

• Explainable AI • Previous Articles     Next Articles

Review of Intelligent Device Fault Diagnosis Based on Deep Learning

HUANG Xundi, PANG Xiongwen   

  1. School of Computer Science,South China Normal University,Guangzhou 510631,China
  • Received:2022-05-23 Revised:2022-09-20 Online:2023-05-15 Published:2023-05-06
  • About author:HUANG Xundi,born in 1998,postgra-duate,is a member of China Computer Federation.His main research interests include deep learning and intelligent fault diagnosis.
    PANG Xiongwen,born in 1972,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include data integration,data mining and big data.
  • Supported by:
    National Natural Science Foundation of China(U1911401) and Guangdong Big Data Science Center Project(U1911401).

Abstract: Intelligent fault diagnosis applies deep learning theory to equipment fault diagnosis,which can automatically identify the health state and fault type of equipment,and has attracted extensive attention in the field of equipment fault diagnosis.Intelligent equipment fault diagnosis realizes equipment fault diagnosis by building end-to-end AI models and algorithms to associate equipment monitoring data with machine health status.However,there are many models and algorithms for equipment fault diagnosis,but they are not common to each other.Using models that are inconsistent with monitoring data for fault diagnosis will lead to a significant decline in diagnosis accuracy.In order to solve this problem,based on the comprehensive investigation of the relevant literature of equipment fault diagnosis,this paper first briefly describes the model framework of in-depth equipment fault diagnosis,then classifies,lists,compares and summarizes the models and algorithms according to the specific application scenarios and equipment monitoring data types,and finally analyzes the future development direction according to the existing problems.This review is expected to provide a useful reference for the research of intelligent equipment fault diagnosis.

Key words: Equipment fault diagnosis, Deep learning, Feature extraction, Fault diagnosis algorithm, Intelligent fault diagnosis

CLC Number: 

  • TP399
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