LSTM Encoder-Decoder方法预测设备剩余使用寿命

应用LSTM Encoder-Decoder提出了机械设备剩余使用寿命预测方法；对获取的传感器数据进行预处理，利用LSTM Encoder对数据序列进行编码，得到设备状态信息的中间表示，其中蕴含了设备状态的特征信息，利用LSTM Decoder对中间表示信息进行解码，利用解码后的信息预测剩余使用寿命；研究了LSTM Encoder-Decoder方法在公开的C-MAPSS数据集上的剩余使用寿命预测试验，与LSTM、D-LSTM等方法进行了对比试验；研究了不同滑动窗口大小对于剩余寿命预测结果的影响。研究结果表明:LSTM Encoder-Decoder方法的剩余使用寿命预测结果的评分函数值和均方根误差均优于LSTM、D-LSTM方法；在FD001子集上，LSTM Encoder-Decoder方法、LSTM方法和D-LSTM方法对应的均方根误差分别为11、12、16；当滑动窗口大小为30时，LSTM Encoder-Decoder方法在FD001~FD004子集对应的评分函数值分别为164、3 012、372、4 800，对应的均方根误差分别为11、20、14、22；当滑动窗口大小为40时，LSTM Encoder-Decoder方法在FD001~FD004子集对应的评分函数值分别为305、1 220、408、4 828，对应的均方根误差分别为14、16、15、19。可见，提出的LSTM Encoder-Decoder方法是一种有效的预测机械设备剩余使用寿命方法，并且滑动窗口大小对于剩余使用寿命预测结果存在一定的影响。 

Remaining useful life prediction for equipment based on LSTM encoder-decoder method

A remaining useful life (RUL) prediction model of mechanical equipment was established based on the long short-term memory (LSTM) encoder-decoder method. The acquired sensor data were preprocessed. The data sequence was coded using the LSTM encoder method. An intermediate representation of the equipment status information was obtained. The characteristic information of the equipment status was obtained in the intermediate representation of the equipment status information. The intermediate representation information was decoded using the LSTM decoder method, and the RUL was predicted using the decoded information. RUL prediction experiments of the LSTM encoder-decoder method on open C-MAPSS data sets were performed. The LSTM encoder-decoder method was compared with the LSTM method, deep-LSTM (D-LSTM) method, and other methods. The effect of the sliding window size on RUL prediction results was evaluated. Research results show that scoring function values and root mean square error (RMSE) evaluation indexes of the RUL prediction results of the LSTM encoder-decoder method are more accurate than those of the LSTM method and D-LSTM method. In the FD001 subset, the RMSEs of the LSTM encoder-decoder method, LSTM method, and D-LSTM method are 11, 12, and 16, respectively. When the sliding window size is 30, the scoring function values corresponding to the FD001-FD004 subsets of the LSTM encoder-decoder method are 164, 3 012, 372, and 4 800, and the corresponding RMSEs are 11, 20, 14, and 22. When the sliding window size increases to 40, the respective scoring function values are 305, 1 220, 408, and 4 828, and the corresponding RMSEs are 14, 16, 15, and 19. Therefore, the proposed method based on the LSTM encoder-decoder effectively predicts the RUL of mechanical equipment, and the sliding window size significantly influences the RUL prediction results. 4 tabs, 6 figs, 32 refs.