Multivariate analysis is an important kind of method in process monitoring and fault detection, in which the canonical correlation analysis (CCA) makes use of the correlation change between two groups of variables to distinguish the system status and has been greatly studied and applied. For the monitoring of nonlinear dynamic systems, the deep neural network-aided CCA (DNN-CCA) has received much attention recently, but it lacks a general definition and comparative study of different network structures. Therefore, this article first introduces four deep neural network (DNN) models that are suitable to combine with CCA, and the general form of DNN-CCA is given in detail. Then, the experimental comparison of these methods is conducted through three cases, so as to analyze the characteristics and distinctions of CCA aided by each DNN model. Finally, some suggestions on method selection are summarized, and the existed open issues in the current DNN-CCA form and future directions are discussed.

中文翻译：

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基于深度神经网络辅助典型相关分析的过程监控和故障检测方法的比较研究


多元分析是过程监控和故障检测中的一种重要方法，其中典型相关分析（CCA）利用两组变量之间的相关性变化来区分系统状态，并得到了大量的研究和应用。针对非线性动态系统的监测，深度神经网络辅助CCA（DNN-CCA）近年来备受关注，但缺乏对不同网络结构的一般定义和比较研究。因此，本文首先介绍了四种适合与CCA结合的深度神经网络（DNN）模型，并详细给出了DNN-CCA的一般形式。然后通过三个案例对这些方法进行实验比较，分析各DNN模型辅助的CCA的特点和区别。最后，总结了方法选择的一些建议，并讨论了当前 DNN-CCA 形式中存在的开放问题和未来的方向。