结合GCN与LSTM的滚动轴承剩余寿命预测方法

摘要/Abstract

摘要： 针对滚动轴承剩余寿命预测因振动信号的非线性和非平稳性而缺乏刻画健康状态可靠指标的问题,提出了将图卷积网络与长短期记忆网络结合的预测方法.首先,对轴承振动信号进行经验模态分解得到内涵模态分量,并且对内涵模态分量进行归一化处理后计算邻接矩阵和特征矩阵;其次,将邻接矩阵和特征矩阵作为图卷积网络的输入,捕获数据关系,挖掘深层特征;然后,将深层特征和内涵模态分量输入长短期记忆网络从而实现时序关系建模,构建健康指标;最后,使用移动平均滤波消除振荡,对健康指标进行多项式拟合,并且计算达到阈值时刻,确定轴承剩余寿命.同时,以IEEE PHM 2012数据挑战赛数据集和XJTU-SY滚动轴承加速实验数据集为对象,验证所提方法.结果表明,使用图卷积网络与长短期记忆网络结合的模型构建健康指标进行滚动轴承剩余寿命预测时,预测结果能够较好地接近真实值,在实际应用中具有一定优势.

关键词: 滚动轴承, 图卷积网络, 长短期记忆网络, 剩余寿命预测

Abstract: Aiming at the problem that the prediction of remaining useful life (RUL) of rolling bearings lacks reliable indicators to describe their health status due to the nonlinearity and non-stationarity of vibration signals, a prediction method combining graph convolutional network (GCN) and long short-term memory (LSTM) networks is proposed in this paper. Firstly, the bearing vibration signal is decomposed by empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMF), followed by normalization of the IMFs, and computation of adjacency and characteristic matrix; Secondly, the adjacency and feature matrix are used as the inputs of GCN to capture the data relationship and mine the deep features. Then, these deep features, along with the IMFs, are input into the LSTM network to realize time series relationship modeling and construct health indicators (HI). Finally, after using the moving average filter to eliminate the oscillation, the HI was polynomial fitted to calculate the threshold moment and determine the bearing RUL. Taking the IEEE PHM 2012 data challenge data set and XJTU-SY rolling bearing accelerated test data set as objects, the proposed method is verified. The experimental results show that when using HI constructed by GCN-LSTM model for rolling bearing RUL prediction, the prediction results closely approximate the real value, which has certain advantages in practical application.

Key words: rolling bearing, graph convolution network, long short-term memory network, remaining useful life prediction

中图分类号:

杜先君, 刘聪. 结合GCN与LSTM的滚动轴承剩余寿命预测方法[J]. 兰州理工大学学报, 2024, 50(3): 42-50.

DU Xian-jun, LIU Cong. Method of predicting remaining useful life of rolling bearing combining GCN and LSTM[J]. Journal of Lanzhou University of Technology, 2024, 50(3): 42-50.

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