College of Electrical and Power Engineering, Taiyuan University of Technology
Department of Chemical Engineering, Chung Yuan Christian University
State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd.

【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(temporalconvolutionalnetwork,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(softthresholdwithattentionmecha-nism,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 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.

山西省自然科学基金面上资助项目(20210302123189)

王思远,陈荣辉,顾凯,等.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.

WANG Siyuan,CHEN Junghui,GU Kai,et al.Short-term fault prediction method for bearing based on SA-TCN[J].Journal of Taiyuan University of Technology,2024,55(1):214-222.