基于MHA与LSTM的滚动轴承性能退化趋势预测

Degradation trend prediction of rolling bearing based on MHA and LSTM

滚动轴承是机械传动设备的重要组成部件,对其进行性能退化趋势预测是保障设备安全稳定运行的关键。为了提高滚动轴承性能退化趋势预测的准确性,提出一种多头注意力机制(multi-head-attention,MHA)与长短期记忆网络(long short-term memory,LSTM)相结合的滚动轴承性能退化趋势预测方法。首先构建时域、频域、时频域和威布尔参数的多域特征,并根据综合性能退化指标对多域特征进行筛选。其次,采用注意力机制增强关键特征的权重,并采用PCA进行特征融合,进一步采用LSTM模型预测滚动轴承性能退化趋势。最后,采用NSF I/UCR中心的轴承疲劳寿命实验数据对本文所提出的方法进行验证,并与其他几种模型进行对比分析,表明本文所提出的方法可以更加准确地预测滚动轴承性能退化趋势。

Rolling bearing is an important component of mechanical transmission equipment.The prediction of its performance degradation trend is the key to ensure the safe and stable operation of the equipment.In order to improve the accuracy of rolling bearing performance degradation trend prediction,a rolling bearing performance degradation trend prediction method based on the combination of multi-head-attention(MHA)and long short-term memory(LSTM)is proposed.Firstly,the multi domain features of time domain,frequency domain,time-frequency domain and Weibull parameters are constructed,and the multi domain features are screened according to the comprehensive performance degradation index.Secondly,the attention mechanism is used to enhance the weight of key features,PCA is used for feature fusion,and LSTM model is further used to predict the performance degradation trend of rolling bearing.Finally,the method proposed in this paper is verified by using the bearing fatigue life experimental data of NSF I/UCR center,and compared with several other models,which shows that the method proposed in this paper can more accurately predict the performance degradation trend of rolling bearing.