摘要
针对现有的滚动轴承退化指标单调性差,对轴承异常不敏感导致基于数据驱动的深度学习算法难以实现轴承寿命准确预测的问题,提出一种基于动态时间规整算法(DTW)和双向长短期记忆神经网络(Bi-LSTM)的滚动轴承剩余寿命预测算法。利用信息熵提取滚动轴承的退化特征,构造连续的时间序列;划分时间序列并构造出参考模板及测试模板,采用DTW算法计算模板间的相似度,将它作为健康指标表征轴承的退化程度;用健康指标训练Bi-LSTM网络并预测轴承的寿命。采用法国IEEE PHM 2012的全寿命数据进行研究,结果表明:经过DTW算法优化信息熵指标后所构造的健康指标可更好地反映轴承的退化过程;当进行寿命预测并保持预测算法不变时,经过优化后的指标较优化前相比,能明显提高轴承寿命预测的准确度。
An algorithm based on the similarity of dynamic time warping(DTW) and Bi-directional long short-term memory(Bi-LSTM) neural network was proposed to solve the problems such as the existed degradation indexes were poorly monotonic and insensitive to bearing anomaly, which would make it difficult for data-driven deep learning algorithms to achieve accurate prediction of bearing life. The information entropy was used to extract the degraded information of rolling bearing, and a continuous time series was constructed;reference templates and test templates were constructed for similarity calculation through time series division, the DTW algorithm was used to calculate the similarity between templates, which was used as a health index to describe the degree of bearing degradation;the Bi-LSTM network was trained with health index and predicted the lifetime of the bearings.The research was carried out by using the full life data of the French IEEE PHM 2012. The result shows that the entropy index optimized by DTW algorithm can better reflect the degradation process of rolling bearing;compared with entropy index before optimization, the optimized index can significantly improve the accuracy of bearing life prediction when the prediction algorithm is kept unchanged during life prediction of rolling bearing.
作者
周建清
朱文昌
王恒
ZHOU Jianqing;ZHU Wenchang;WANG Heng(School of Electrical Engineering,Changzhou Vocational College of Technology,Changzhou Jiangsu 213161,China;School of Mechanical Engineering,Nantong University,Nantong Jiangsu 226019,China)
出处
《机床与液压》
北大核心
2022年第22期179-184,共6页
Machine Tool & Hydraulics
基金
国家自然科学基金青年科学基金项目(51405246)
南通市基础科学研究项目(JC2021023)。
关键词
滚动轴承
剩余寿命预测
动态时间规整
相似度
双向长短期记忆神经网络
Rolling bearing
Remaining life prediction
Dynamic time warping
Similarity
Bi-directional long short-term memory neural network
