太原理工大学学报2024，Vol.55Issue(1)：214-222,9.DOI:10.16355/j.tyut.1007-9432.20230117
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
摘要
Abstract
[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 eventu-ally 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Key words
short-term failure prediction/temporal convolutional network/bearing/attention mechanism分类
信息技术与安全科学引用本文复制引用
王思远,陈荣辉,顾凯,任密蜂,阎高伟..基于SA-TCN的轴承短期故障预测方法[J].太原理工大学学报,2024,55(1):214-222,9.基金项目
山西省自然科学基金面上资助项目(20210302123189) （20210302123189）
