AGRU-TCDN:Temporal Convolutional Denoising Network for Intelligent Fault Diagnosis
作者单位：Colleage of Information EngineeringZhejiang University of Technology
会议名称：《第32届中国过程控制会议(CPCC2021)》
会议日期：2021年
学科分类：12[管理学] 08[工学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 080203[工学-机械设计及理论] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0838[工学-公安技术] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Temporal Convolutional Network(TCN) Convolutional Denoising Auto-Encoder(CDAE) Gated Recurrent Unit(GRU) Attentional Mechanism Fault Diagnosis
摘 要：Rolling bearing is an important component of electromechanical *** to long-term service in harsh environments such as high speeds and heavy loads,once the failure occurs,the healthy operation of the equipment will be affected at least,and the serious accident will be caused,resulting in huge economic losses and *** order to realize real-time monitoring of the health state of rolling bearings in a high-noise environment,this paper is concerned with the proposition of a new deep learning framework of temporal convolutional denoising network with attention gated recurrent unit(AGRU-TCDN).AGRU-TCDN performs denoising processing on rolling bearing vibration signals through convolutional denoising auto-encoder(CDAE),and then the reconstructed signal is input to the temporal convolutional network(TCN) and attention gated recurrent unit(AGRU),capturing the long-term dependent information of vibration *** effectiveness of this method is verified by experiments with different degrees of noise.