基于CNN-LSTM的QAR数据特征提取与预测

针对传统数据驱动的故障诊断方法难以从QAR数据中提取有效特征的问题，提出一种融合卷积神经网络（convolutional neural network，CNN）与长短时记忆网络（long short-term memory，LSTM）的双通道融合模型CNN-LSTM。CNN与LSTM分别作为两个通道，通过注意力机制（attention）融合，从而使模型能同时表达数据在空间维度和时间维度上的特征，并以时间序列预测的方式验证融合模型特征提取的有效性。实验结果表明，双通道融合模型与单一的CNN、LSTM相比，能够更有效地提取数据特征，模型单步预测与多步预测误差平均降低35.3%。为基于QAR数据的故障诊断提供一种新的研究思路。

Feature extraction and prediction of QAR data based on CNN-LSTM

Aiming at the problem that it is difficult for the traditional fault diagnosis method to extract effective features from QAR data, this paper proposed a dual-channel fusion model called CNN-LSTM, which combined the CNN and the LSTM. Respectively as two channels, it fused CNN and LSTM through the attention mechanism to make the model be able to simultaneously express the features of the data in both space dimension and time dimension. And it alse verified the validity of the feature extraction of the fusion model through time series prediction. Results of the experiment show that when compared with single CNN or LSTM, the dual-channel fusion model can extract data features more effectively, make the errors of both single-step prediction and multi-step prediction reduce by an average of 35.3%. It provide a new research idea for fault diagnosis based on QAR data.