状态划分下基于Bi-LSTM-Att的轴承剩余寿命预测

Prediction of Bearing Residual Life Based on Bi-LSTM-Att Under State Partition

准确预测滚动轴承剩余使用寿命(remaining useful life,RUL),对于保证工程设备安全稳定可靠运行具有极其重要的作用.现有深度学习预测方法往往直接建立振动监测数据与剩余寿命之间的映射关系,通常忽略滚动轴承性能退化的不同状态差异性,且并未考虑深度学习模型所提取各类特征的差异性,给剩余寿命预测结果带来了极大的偏差.鉴于此,提出一种新型滚动轴承退化状态划分方法和RUL预测方法.提取轴承振动信号的特征,利用Mann-Kendall检验法进行趋势判断,确定出退化期的起始点;通过归一化奇异值相关系数走势确定出慢速退化期的终点;构建基于融合注意力机制的双向长短时记忆网络(bidirectional long short-term memory with attention,Bi-LSTM-Att)的滚动轴承RUL预测模型,利用所截取的慢速退化期数据与对应RUL标签训练预测模型实现RUL预测.通过轴承公开数据集验证所提方法对轴承RUL预测的准确性和有效性.

Accurate prediction of the Remaining Useful Life(RUL)of rolling bearings is of paramount importance for ensuring the safe,stable,and reliable operation of engineering equipment.Existing deep learning prediction methods often directly establish a mapping relationship between vibration monitoring data and RUL,typically overlooking the differential states of rolling bearing performance degradation and neglecting the diversity of features extracted by deep learning models,leading to significant bias in RUL prediction results.In light of this,a novel method for dividing the degradation state of rolling bearings and predicting RUL is proposed.Features of bearing vibration signals are extracted,and the Mann-Kendall test is employed to judge trends,determining the starting point of the degradation period.The endpoint of the slow degradation period is identified through the trend of normalized singular value correlation coefficients.A rolling bearing RUL prediction model based on a bidirectional long short-term memory network with attention(Bi-LSTM-Att)is constructed,and the slow degradation period data and corresponding RUL labels are used to train the prediction model to achieve RUL prediction.The accuracy and effectiveness of the proposed method for bearing RUL prediction are validated through a public bearing dataset.