基于多任务门控网络的滚动轴承寿命预测方法  

A rolling bearing life prediction method based on multi-task gated networks

作　　者：宋浏阳 郑传浩 金烨 林天骄 韩长坤 王华庆 SONG Liuyang;ZHENG Chuanhao;JIN Ye;LIN Tianjiao;HAN Changkun;WANG Huaqing*(Beijing Key Laboratory of Health Monitoring and Self Recovery for High-End Mechanical Equipment,Beijing University of Chemical Technology,Beijing 100029;National Key Laboratory of High-end Compressor and System Technology,Beijing University of Chemical Technology,Beijing 100029)

机构地区：[1]北京化工大学高端机械装备健康监控与自愈化北京市重点实验室,北京100029 [2]北京化工大学高端压缩机及系统技术全国重点实验室,北京100029

出　　处：《中国舰船研究》2025年第2期107-117,共11页Chinese Journal of Ship Research

基　　金：国家自然科学基金面上项目(52375076);北京市科技新星计划(20240484559)。

摘　　要：[目的]为实现船舶机械设备中轴承的剩余寿命预测,提出基于双向门控循环单元(BiGRU)、变分自编码器(VAE)和多门控专家混合层(MMoE)的多任务门控网络预测模型。[方法]首先,计算轴承信号时域特征以表征监测数据中的基本退化趋势;然后,建立轴承健康状态(HS)评估和剩余使用寿命(RUL)预测子任务构成多任务门控网络预测模型,子任务中使用BiGRU和VAE提取时域特征趋势信号中的退化信息,再利用MMoE自适应分离子任务的差异特征。最后,在XJTU-SY轴承数据集上进行有效性验证。[结果]结果表明,与长短期记忆网络(LSTM)等经典时序数据预测模型相比,多任务门控网络预测模型的预测精度更高,误差指标MAE和RMSE分别提升62.5%和67.81%。[结论]所提方法可以实现轴承剩余寿命的预测,对船舶机械设备健康管理与智能运维具有一定的参考价值。[Objective]To achieve the remaining life prediction of bearings in ship mechanical equipment,a multi-task gated networks prediction model based on the Bidirectional Gated Recurrent Unit(BiGRU),Variational Autoencoder(VAE),and Multi-gate Mixture-of-Experts(MMoE)is proposed.[Methods]First,the time-domain features of the bearing signals are calculated to characterize the basic degradation trends in the monitoring data.Then,a multi-task gated networks prediction model composed of bearing Health State(HS)assessment and Remaining Useful Life(RUL)prediction subtasks is established.In the subtasks,BiGRU and VAE are used to extract the degradation information from the trend signals of the time-domain features,and then MMoE is utilized to adaptively separate the distinctive features of the subtasks.Finally,the effectiveness is verified on the XJTU-SY bearing dataset.[Results]The results show that,compared with classic timeseries data prediction models such as Long Short Term Memory(LSTM),the multi-task gated networks prediction model has higher prediction accuracy,with the error metrics Mean Absolute Error(MAE)and Root Mean Square Error(RMSE)improved by 62.5% and 67.81% respectively.[Conclusion]The proposed method can achieve the prediction of the remaining life of bearings and has certain reference value for the health management and intelligent operations and maintenance(O&M)of ship mechanical equipment.

关 键 词：船舶设备 轴承 剩余寿命预测 多任务门控网络预测模型 

分 类 号：U664.21[交通运输工程—船舶及航道工程]