Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects the performance of fault diagnosis. In this paper, by synthetically considering feature extraction and time delay of occurrence of faults, we propose a novel fault diagnosis method that consists of two parts, namely, sliding window processing and CNN-LSTM model based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, samples obtained from multivariate time series by the sliding window processing integrates feature information and time delay information. Then, the obtained samples are fed into the proposed CNN-LSTM model including CNN layers and LSTM layers. The CNN layers perform feature learning without relying on prior knowledge. Time delay information is captured with the use of the LSTM layers. The fault diagnosis of the Tennessee Eastman chemical process is addressed, and it is verified that the predictive accuracy and noise sensitivity of fault diagnosis can be greatly improved when the proposed method is applied. Comparisons with five existing fault diagnosis methods show the superiority of the proposed method.

故障诊断在实际生产活动中起着重要作用。由于可以高效，经济地收集大量数据，因此基于深度学习的数据驱动方法由于其在特征提取方面的优势而获得了复杂系统故障诊断的显着结果。然而，现有技术很少考虑故障发生的时间延迟，这会影响故障诊断的性能。本文综合考虑故障特征提取和故障发生时延，提出了一种新的故障诊断方法，该方法包括滑动窗口处理和基于卷积神经网络（CNN）组合的CNN-LSTM模型两部分。 ）和长期短期记忆网络（LSTM）。首先，通过滑动窗口处理从多元时间序列获得的样本整合了特征信息和时延信息。然后，将获得的样本输入到包括CNN层和LSTM层的拟议的CNN-LSTM模型中。CNN层在不依赖现有知识的情况下执行特征学习。使用LSTM层捕获时间延迟信息。解决了田纳西州伊士曼化学过程的故障诊断问题，并证明了该方法的应用可以大大提高故障诊断的预测精度和噪声敏感性。与五种现有故障诊断方法的比较表明了该方法的优越性。获得的样本将被馈送到包含CNN层和LSTM层的拟议的CNN-LSTM模型中。CNN层在不依赖现有知识的情况下执行特征学习。使用LSTM层捕获时间延迟信息。解决了田纳西州伊士曼化学过程的故障诊断问题，并证明了该方法的应用可以大大提高故障诊断的预测精度和噪声敏感性。与五种现有故障诊断方法的比较表明了该方法的优越性。将获得的样本输入到建议的CNN-LSTM模型中，该模型包括CNN层和LSTM层。CNN层在不依赖现有知识的情况下执行特征学习。使用LSTM层捕获时间延迟信息。解决了田纳西州伊士曼化学过程的故障诊断问题，并证明了该方法的应用可以大大提高故障诊断的预测精度和噪声敏感性。与五种现有故障诊断方法的比较表明了该方法的优越性。应用该方法可以大大提高故障诊断的预测精度和噪声敏感性。与五种现有故障诊断方法的比较表明了该方法的优越性。应用该方法可以大大提高故障诊断的预测精度和噪声敏感性。与五种现有故障诊断方法的比较表明了该方法的优越性。