基于卷积自编码器和时间卷积网络的轴承性能退化趋势预测

刘渊博,陈相,刘妤

振动与冲击 ›› 2023, Vol. 42 ›› Issue (13) : 214-225.

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (13) : 214-225.
论文

基于卷积自编码器和时间卷积网络的轴承性能退化趋势预测

  • 刘渊博,陈相,刘妤
作者信息 +

Prediction of bearing performance degradation trend based on CAE and TCN

  • LIU Yuanbo, CHEN Xiang, LIU Yu
Author information +
文章历史 +

摘要

针对现有的退化预测研究在构建健康指标时面临信息损失,在建立预测模型时并行计算性能差、感受野不大等不足,结合监测对象性能退化的时序特性,提出基于卷积自编码器(CAE)和时间卷积网络(TCN)的性能退化趋势预测方法。首先,构建振动信号多域高维特征集,并采用综合评价指标初步筛选敏感性好、趋势性强的性能退化指标;其次,采用核主成分分析(KPCA)方法消除多域特征之间的冗余信息,并实现基于CAE网络的健康指标构建;在此基础上,构建基于TCN的性能退化预测模型,采用直接多步预测实现退化趋势预测,并利用轴承公用数据集验证方法的有效性。结果表明:采用KPCA可以将特征集从14维降至4维,且保留了原优选特征集97.63%的信息;基于CAE网络构建健康指标的方法是有效的,所构建的健康指标随时间的变化历程能够真实反映轴承性能的退化过程,且该方法相较于自编码网络(AE)和高斯混合模型(GMM)两种常用的健康指标构建方法具有明显优势;基于TCN算法构建的模型能够准确预测轴承的性能退化,该模型相较于基于长短时记忆(LSTM)网络和基于门控循环单元(GRU)等构建的预测模型性能更好,预测精度更高,预测步长为3时的均方根误差和平均绝对误差分别为0.0257和0.0187。该方法具有普遍意义,可推广应用于其它机械装备/零部件的性能退化趋势预测。

Abstract

Aiming at the shortcomings of existing performance degradation prediction research, such as information loss when building health indicator, poor parallel computing performance and small receptive field when building prediction models, combined with the timing characteristics of performance degradation of monitored objects, a performance degradation prediction method based on Convolutional Auto-Encoder (CAE) and Temporal Convolutional Network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indicators with good sensitivity and strong trend. Secondly, the Kernel Principal Component Analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features and realize the construction of health indicator based on CAE network. On this basis, a performance degradation prediction model based on TCN is constructed, and direct multi-step prediction is used to achieve performance degradation trend prediction, and the effectiveness of the method is verified by using the bearing public data set. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 97.63% of the information of the original feature set is retained. Furthermore, the method of constructing health indicator based on CAE network is effective. The change process of the constructed health indicator truly reflects the performance degradation process of the bearing. Compared with the two commonly used health index construction methods, Auto-Encoding (AE) network and Gaussian Mixture Model (GMM), this method has obvious advantages. At the same time, the prediction model based on the TCN algorithm can accurately predict the performance degradation of the bearing, which has better performance and higher prediction accuracy than the prediction model based on the Long Short-Term Memory (LSTM) network and the Gated Recurrent Unit (GRU). The root mean square error and mean absolute error when the prediction step size is 3 are 0.0257 and 0.0187, respectively. This method has general significance and may be extended to predict the performance degradation trend of other mechanical equipment/components.

关键词

退化预测 / 特征提取 / 核主成分分析 / 健康指标 / 时间卷积网络 / 卷积自编码器

Key words

Degradation Prediction / Feature Extraction / Kernel Principal Component Analysis / Health Indicator / Temporal Convolutional Networks / Convolutional Auto-Encoder

引用本文

导出引用
刘渊博,陈相,刘妤. 基于卷积自编码器和时间卷积网络的轴承性能退化趋势预测[J]. 振动与冲击, 2023, 42(13): 214-225
LIU Yuanbo, CHEN Xiang, LIU Yu. Prediction of bearing performance degradation trend based on CAE and TCN[J]. Journal of Vibration and Shock, 2023, 42(13): 214-225

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