基于时间序列数据扩增和BLSTM的滚动轴承剩余寿命预测方法

针对滚动轴承全寿命周期监测数据不足导致剩余寿命预测精度不高的问题，提出一种基于时间序列数据扩增和双向长短时记忆(bidirectional long-short term memory, BLSTM)网络的剩余寿命预测方法。首先，采集训练用滚动轴承全寿命周期振动加速度和测试轴承振动加速度数据。其次，对采集得到的原始数据预处理后提取健康因子，将训练用数据和测试数据分别构成参考数据集和目标数据集。然后，以参考数据集为基础，利用动态时间规整算法扩增目标数据集数据。最后，使用数据扩增后的测试数据训练BLSTM网络，利用训练好的BLSTM网络预测滚动轴承性能退化趋势和剩余寿命。实验结果表明，基于动态时间规整算法的数据扩增模型能够根据已有全寿命周期数据，扩增性能退化过程相似的滚动轴承运行数据，利用扩增数据训练BLSTM网络，能够有效提高性能退化趋势预测能力，进而提高剩余寿命预测精度。

Remaining useful life prediction method of rolling bearing based on time series data augmentation and BLSTM

To solve the problem of low accuracy of remaining useful life prediction due to insufficient monitoring data of the whole life cycle of rolling bearings, a method of remaining useful life prediction based on time series data augmentation and Bidirectional long-term short term memory network(BLSTM) is proposed. Firsty, the data of the vibration acceleration of the training rolling bearing and the test bearing are collected. Secondly, after preprocessing the collected raw data, the health factors are extracted, and the training data and test data are formed into reference data set and target data set respectively. Thirdly, based on the reference data set, the target data set is augmented by the dynamic time warping algorithm.Finally, the BLSTM network is trained with the augmented test data, and the trained BLSTM network is used to predict the performance degradation trend and remaining useful of the rolling bearing.The experimental results show that the data augmentation model based on dynamic time warping algorithm can effectively improve the prediction ability of performance degradation trend and the prediction accuracy of remaining useful life by training BLSTM network based on the existing rolling bearing operation data with similar amplification performance degradation process based on the existing full-life cycle data.