基于双重自注意力机制和长短时记忆网络的剩余寿命预测

Remaining useful life prediction based on double self-attention mechanism and long short-term memory network

剩余使用寿命(remaining useful life,RUL)预测是产品故障预测与健康管理的重要内容。传统长短期记忆(long short-term memory,LSTM)网络无法主动选择关键特征、难以高效提取大数据所蕴含的退化信息。提出一种基于改进LSTM网络的RUL预测方法,采用随机森林(random forest,RF)算法筛选输入特征,以主动选取关键特征;采用双重自注意力机制分别从特征维度和时间维度完成权重自适应分配,使模型在学习过程中关注主要特征和历史时间点;通过融合统计特征,以提高RUL预测精度。以航空发动机数据集为例完成案例分析,验证方法有效性。结果表明,所提方法能有效提高基于复杂数据集的RUL预测精度。

Prediction of remaining useful life(RUL)is an important part of fault prognostic and health management.Traditional long short-term memory(LSTM)network cannot select the key features actively,and it is difficult to effectively extract the degradation information contained in big data.This paper proposes an RUL prediction approach based on an improved LSTM network,where the random forest(RF)algorithm is adopted to filter the input features in order to select key features actively.A double self-attention mechanism is used to complete the adaptive weight assignment from feature dimension and the time dimension.Thus,the proposed approach can focus on the key features and historical time during the learning process.By fusing the statistical features,the model can improve the accuracy of RUL prediction.To illustrate the effectiveness of the proposed method,a case study is conducted with a data set of aircraft engine.The results indicate that the proposed method can effectively improve the accuracy of RUL prediction with complicated data sets.