摘要
针对传统剩余使用寿命(remaining useful life,RUL)预测方法在建模多传感器数据的复杂时空依赖与抗噪性能方面存在不足的问题,提出一种融合时空图卷积网络(spatial-temporal graph convolutional network,STGCN)、软阈值残差注意力机制与双向门控循环单元(bidirectional gated recurrent unit,BiGRU)的剩余寿命预测模型。首先,通过时空图卷积提取多传感器数据中的空间与时间特征,建模设备部件间的拓扑关系与动态演化;其次,引入BiGRU以捕捉深层时序依赖,并结合软阈值残差注意力机制,增强对关键退化特征的感知能力并抑制噪声干扰;最后,实现对机械设备剩余寿命的精准预测。在PHM2010与NASA数据集上的实验表明,该方法在多种噪声干扰下仍具优异预测性能,显著优于现有方法。
In view of the shortcomings of traditional remaining useful life(RUL)prediction methods in modeling the complex spatiotemporal dependencies and noise resistance of multi-sensor data,a RUL prediction model is proposed,integrating a spatial-temporal graph convolutional network(STGCN),a soft-threshold residual attention mechanism,and a bidirectional gated recurrent unit(BiGRU).The model first extracts spatial and temporal features from multi-sensor data through STGCN to model the topological relationship and dynamic evolution between equipment components.Secondly,a BiGRU unit is introduced to capture deep temporal dependencies,and combined with a soft-threshold residual attention mechanism,the perception of key degradation features is enhanced and noise interference is suppressed.Finally,accurate prediction of the RUL of the machine equipment is achieved.Experiments on the PHM2010 and NASA datasets show that this method still has excellent prediction performance under various noise interferences,significantly outperforming existing methods.
作者
刘中民
刘小平
王斌
郝晓龙
张立杰
LIU Zhongmin;LIU Xiaoping;WANG Bin;HAO Xiaolong;ZHANG Lijie(CHN Energy Huanghua Port Company with Limited Liability,Cangzhou 061100,CHN;School of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,CHN)
出处
《制造技术与机床》
北大核心
2025年第9期185-193,共9页
Manufacturing Technology & Machine Tool
关键词
剩余使用寿命
时空图卷积
双向门控循环单元
软阈值
注意力
remaining useful life
spatiotemporal graph convolution
bidirectional gated recurrent unit
soft thresholding
attention
