In recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN‐dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the real‐time monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach.

中文翻译：

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基于DBN和Dropout的TE过程故障诊断方法研究

近年来，深度学习在模式识别和特征提取方面表现出卓越的性能和潜力，吸引了工程研究人员和学者越来越多的关注。基于深度学习的故障诊断方法也已成为大量研究的焦点。提出了一种基于DBN-dropout的非线性过程故障诊断与识别方法。深度信念网络（DBN）在处理非线性过程方面具有显着优势，它可以提取非线性过程数据的抽象表示以构建一个深度网络，以实现对过程操作的实时监控。辍学技术可以减少过度拟合并提高模型的泛化能力。之后，