基于自适应马氏空间与深度学习的滚动轴承退化趋势预测  

Degradation trend prediction of rolling bearing based on adaptive Mahalanobis space and deep learning

作　　者：吴梦蝶 程龙生[1] 陈闻鹤 WU Mengdie;CHENG Longsheng;CHEN Wenhe(School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China;School of Engineering,Lancaster University,LA14YW Lancaster,U.K.)

机构地区：[1]南京理工大学经济管理学院,江苏南京210094 [2]兰卡斯特大学工程学院,英国兰卡斯特LA14YW

出　　处：《系统工程与电子技术》2023年第10期3338-3349,共12页Systems Engineering and Electronics

基　　金：国家留学基金(202206840062)资助课题。

摘　　要：滚动轴承退化趋势预测中,传统特征选择主要依赖人工经验和单一评价算法,容易造成特征的少选或错选,且单一深度学习网络无法充分挖掘数据中包含的性能退化信息,导致模型预测精度较低。针对上述问题,提出一种基于自适应马氏空间(adaptive Mahalanobis space,AMS)与融合深度学习网络的滚动轴承退化趋势预测方法。首先,分解原始信号并利用相关峭度系数准则筛选固有模态函数(intrinsic mode function,IMF)分量重构新信号,从多域视角提取特征;然后,构建基于AMS的多目标特征选择算法自动优选特征,减少人工依赖,加强自适应性和泛化性,并将马氏距离(Mahalanobis distance,MD)与指数加权移动平均(exponential weighted moving average,EWMA)方法进行结合,对轴承性能退化趋势进行良好表征;最后,利用稀疏自动编码器和门控循环单元(sparse auto encoder-gated recurrent unit,SAE-GRU)融合模型进行预测。实验结果表明,所提方法能够有效筛选最优特征,显著提高预测精度。In the prediction of rolling bearing degradation trend,traditional feature selection mainly relies on manual experience and a single evaluation algorithm,which is easy to cause under-selection or misselection of features.Moreover,a single deep learning network cannot fully mine the performance degradation information contained in the data,which results in low prediction accuracy of the model.To solve the above problem,a prediction method of rolling bearing degradation trend based on adaptive Mahalanobis space(AMS)and fusion deep learning network is proposed.Firstly,the original signals are decomposed and the correlated kurtosis coefficient criterion is used to screen the intrinsic mode function(IMF)to reconstruct the new signals,and the features are extracted from the multi-domain perspective.Secondly,the multi-objective feature selection algorithm based on AMS is built to optimize characteristic automatically.With the aim of reducing manual dependencies,and serengthening the adaptability and generalization,Mahalanobis distance(MD)is combined with the exponential weighted moving average(EWMA)method to well characterize the degradation trend of bearing performance.Finally,the fusion model of sparse auto encoder and gated recurrent unit(SAE-GRU)is used for prediction.The experimental results show that the proposed method can effectively screen the optimal features and significantly improve the prediction accuracy.

关 键 词：滚动轴承 自适应马氏空间 多目标特征选择 融合模型 退化趋势预测 

分 类 号：TH17[机械工程—机械制造及自动化]