机电工程技术2024，Vol.53Issue(7)：23-28,6.DOI:10.3969/j.issn.1009-9492.2024.07.005
一种多阶段机械装备剩余寿命预测新方法及应用
A Novel Multi-stage Approach and Application of Remaining Life Prediction for Mechanical Equipment
摘要
Abstract
A new multi-stage remaining life prediction method based on deep transfer learning is proposed to address the issue of low accuracy in predicting the remaining life of mechanical equipment.The core idea of this method is to first determine the health status of mechanical equipment,and then activate the warning and remaining life prediction mechanism for equipment that has entered degradation status.This can greatly improve the prediction accuracy of remaining life.In the first stage,a combination of convolutional autoencoder and Pearson correlation coefficient is used to establish health indicators,and online health recognition is carried out through fast search and discovery of density peak clustering methods;In the second stage,the fault data is input into a multi-channel transferable bidirectional long short-term memory network prediction model,and the feature distribution differences are gradually reduced by adding domain adaptation modules to obtain the optimal training model and obtain regression results with more generalization ability.Taking the IEEE2012PHM bearing life dataset as an example,the minimum prediction error is achieved compared with other related methods.The experimental results verify the effectiveness and accuracy of the proposed approach,without the need for manual threshold setting,which has high promotion and application value.关键词
机械装备/深度迁移学习/多阶段/剩余寿命预测Key words
mechanical equipment/deep transfer learning/multi-stage/remaining useful life prediction分类
机械制造引用本文复制引用
吕明珠..一种多阶段机械装备剩余寿命预测新方法及应用[J].机电工程技术,2024,53(7):23-28,6.基金项目
2021年度辽宁省教育厅高等学校基本科研项目(重点项目)(LJKZ1286) （重点项目）
2021年度辽宁省教育厅高等职业教育开放办学合作项目(2021360-191) （2021360-191）
