基于深度学习的智能设备故障诊断研究综述

doi:10.11896/jsjkx.220500197

摘要/Abstract

摘要： 智能设备故障诊断技术(Intelligent Fault Diagnosis,IFD)将深度学习理论应用于设备故障诊断,能自动识别设备的健康状态和故障类型,在设备故障诊断领域引起了广泛关注。智能设备故障诊断通过构建端到端的AI模型和算法将设备监测数据与机器健康状态关联以实现设备故障诊断,但设备故障诊断的模型和算法较多且相互之间并不通用,采用与监测数据不相符的模型进行故障诊断会导致诊断准确率大幅度下滑。为解决这一问题,在全面调查设备故障诊断相关文献的基础上,首先简述深度设备故障诊断的模型框架,再根据具体应用场景和设备监测数据类型对模型算法进行分类介绍、列表对比及总结,最后针对存在的问题分析了未来的发展方向。本综述有望为智能设备故障诊断的研究提供有益的参考。

关键词: 设备故障诊断, 深度学习, 特征提取, 故障诊断算法, 智能故障诊断

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

中图分类号:

TP399

黄迅迪, 庞雄文. 基于深度学习的智能设备故障诊断研究综述[J]. 计算机科学, 2023, 50(5): 93-102. https://doi.org/10.11896/jsjkx.220500197

HUANG Xundi, PANG Xiongwen. Review of Intelligent Device Fault Diagnosis Based on Deep Learning[J]. Computer Science, 2023, 50(5): 93-102. https://doi.org/10.11896/jsjkx.220500197

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