基于SA-TCN的轴承短期故障预测方法  

Short-term Fault Prediction Method for Bearing Based on SA-TCN

作　　者：王思远 陈荣辉[2] 顾凯 任密蜂 阎高伟 WANG Siyuan;CHEN Junghui;GU Kai;REN Mifeng;YAN Gaowei(College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan 030024,China;Department of Chemical Engineering,Chung Yuan Christian University,Taoyuan 320-338,China;State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment,China Nuclear Power Engineering Co.,Ltd.,Shenzhen 518172,China)

机构地区：[1]太原理工大学电气与动力工程学院,太原030024 [2]台湾中原大学化学工程学系,中国台湾桃园320-338 [3]中广核工程有限公司核电安全监控技术与装备国家重点实验室,广东深圳518172

出　　处：《太原理工大学学报》2024年第1期214-222,共9页Journal of Taiyuan University of Technology

基　　金：山西省自然科学基金面上资助项目(20210302123189)。

摘　　要：【目的】轴承是制造业中的核心零部件之一,其健康状况决定着主机设备的安全性,对轴承进行短期故障预测可以有效预防生产设备故障的发生。【方法】针对轴承短期故障预测未能实现端到端的问题,提出一种基于时序卷积网络(temporal convolutional network,TCN)的短期故障预测方法。该网络能通过当前时刻监测到的数据直接输出轴承最终会发生的故障类型以及下一时刻将要处于的劣化阶段。此外,提出软阈值注意力机制(soft threshold with attention mechanism,SA)解决轴承工作环境存在背景噪声或采集数据的过程中有噪声干扰的问题;在短期故障预测过程中,注意力机制根据TCN网络的预测目标自适应生成软阈值,软阈值作用于TCN提取到的时空特征,以达到降低噪声影响的目的。【结果】实验结果表明所提算法准确率高,具有较高的实际工程应用价值。【Purposes】Bearing is one of the core components in the manufacturing industry.Its health status determines the safety of the host.Short-term failure prediction can effectively ensure the smooth progress of the industrial production process.【Methods】In order to solve the end-to-end problem,a temporal convolutional network(TCN)based short-term fault prediction strategy was proposed.The network could directly output the types of failure that would eventually occur in the bearing and the degradation stage that would be in the next moment through the data monitored at the current moment.In addition,soft threshold with attention mechanism is proposed to solve the problem of background noise in the working environment of bearings or noise interference in the process of data acquisition.During the short-term fault prediction process,the attention mechanism adaptively generates a soft threshold according to the prediction target of the TCN network,and the soft threshold acts on the spatiotemporal features extracted by the TCN to achieve the purpose of reducing noise impact.【Findings】The experimental results show that the proposed algorithm has high accuracy,which verifies the effectiveness and high practical engineering application value of the proposed algorithm.

关 键 词：短期故障预测 时序卷积网络 轴承 注意力机制 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP206[自动化与计算机技术—控制科学与工程]