基于信号分解深度网络的轴承剩余寿命预测

Bearing remaining useful life prediction based on signal decomposition embedding deep network

工况波动、噪声干扰等因素造成滚动轴承高频振动信号信息冗杂,使退化过程建模结果难以准确反映实际退化趋势,导致轴承剩余寿命预测准确性不高。为此,本文提出一种基于信号分解网络的轴承剩余寿命长程时序相关预测方法。运用时间序列分解算法将振动信号分解为趋势、周期及余项3种分量,去除冗杂信息;针对快速退化到失效阶段,建立基于长短期记忆网络的自编码器特征提取模型,获得单调性和趋势性强的健康指标;最后,建立深度时序自回归神经网络模型对健康指标进行趋势预测,输出剩余寿命预测值的概率分布。实验结果表明,本文所构建的健康指标具有良好的趋势性和单调性,相比其他相关方法,所提剩余寿命预测方法具有更高准确率。

Working condition fluctuations,noise interference and other factors result in complex information in the high-frequency vibration signals of rolling bearings,making it difficult for the modeling of degradation processes to accurately reflect actual degradation trends,thus reducing the accuracy of remaining life predictions for the bearings.To address this issue,this paper proposes a long-range time-series correlation prediction method for bearing remaining life based on a signal decomposition network.The method uses a time series decomposition algorithm to break down vibration signals into trend,periodic,and residual components,effectively filtering out redundant information.For the rapid degradation to failure stages,a feature extraction model based on a long short-term memory network autoencoder is designed to derive health indicators with strong monotonicity and trend.Finally,a deep temporal autoregressive neural network model is developed to predict trends in these health indicators and to output the probability distribution of remaining life predictions.Experimental results show that the health indicators constructed in this study exhibit strong trends and monotonicity.Compared to other methods,the proposed remaining life prediction method achieves significantly higher accuracy.