摘要
滚动轴承是大量旋转机械中的关键部件,其剩余使用寿命(Remaining Useful Life,RUL)预测问题关系到设备能否安全稳定运行。为解决目前RUL预测精度低的问题,提出一种在频域上结合时间卷积自编码器(Temporal Convolutional Autoencoder,TCAE)和Informer网络的滚动轴承RUL预测方法(TCAE-Informer)。所提方法设计了一种TCAE,面向滚动轴承不同时间样本的频域信号,自适应地挖掘更能反映滚动轴承全寿命退化周期的深度特征;搭建起一个Informer网络模型,借助其在长序列信息上的学习优势,有效拟合出深度特征与滚动轴承RUL的映射关系,进而实现滚动轴承RUL预测功能。使用XJTU-SY轴承数据集的对比验证,对照3种RUL预测结果评价指标,所提方法在不同的工况条件下,相比现有的多种方法均能够实现较为准确的RUL预测效果,证明了所提方法具有优越的RUL预测能力和泛化能力。针对不同方法进行了抗干扰测试,所提方法在不同噪声条件下均展现出了更优的RUL预测效果,证明了所提方法具有良好的RUL预测抗干扰能力。
Rolling bearing is a key component in a large number of rotating machines,and its remaining useful life(RUL)prediction is related to whether an equipment can operate safely and stably.In order to solve the current problem of low accuracy of RUL prediction,a prediction method combining temporal convolutional autoencoder(TCAE)and Informer(TCAE-Informer)network in frequency domain is proposed for RUL of rolling bearings.The method designs a TCAE for the frequency-domain signals of different time samples of rolling bearings,and adaptively extracts the depth features that better reflect the full-life degradation law of rolling bearings.An Informer network model is built,and with the help of its learning advantage in long sequence information,the mapping relationship between the depth features and the RUL of rolling bearings is effectively fitted to realize the RUL prediction function of rolling bearings.XJTU-SY bearing dataset is used to compare and validate the evaluation indexes of three RUL prediction results.The results show that the proposed method is able to achieve more accurate RUL prediction effect under different working conditions compared with many existing methods,which proves that it has superior RUL prediction ability and generalization ability.The anti-interference test is carried out for different methods.In the test,the proposed method shows better RUL prediction effect under different noise conditions,which proves that it has superior anti-interference ability of RUL prediction.
作者
闫昊
李思雨
展先彪
董恩志
温亮
贾希胜
YAN Hao;LI Siyu;ZHAN Xianbiao;DONG Enzhi;WEN Liang;JIA Xisheng(Shijiazhuang Campus,Army Engineering University of PLA,Shijiazhuang 050003,Hebei,China;School of Electronic and Control Engineering,North China Institute of Aerospace Engineering,Langfang 065000,Hebei,China)
出处
《兵工学报》
北大核心
2025年第S1期408-418,共11页
Acta Armamentarii
