In the processing and manufacturing industries, there has been a large push to produce higher quality products and ensure maximum efficiency of processes, which requires approaches to effectively detect and resolve disturbances to ensure optimal operations. While many types of disturbances can be compensated by a control system, it cannot handle some large process disruptions. As such, it is important to develop monitoring systems to effectively detect and identify those faults such that they can be quickly resolved by operators. This article proposes a novel probabilistic fault detection and identification method which adopts a newly developed deep learning approach using Bayesian recurrent neural networks (BRNNs) with variational dropout. The BRNN model is general and can model complex nonlinear dynamics. Moreover, compared to traditional statistic-based data-driven fault detection and identification methods, the proposed BRNN-based method yields uncertainty estimates which allow for simultaneous fault detection of chemical processes, direct fault identification, and fault propagation analysis. The performance of the method is demonstrated and contrasted to (dynamic) principal component analysis, which is widely applied in the industry, in the benchmark Tennessee Eastman process (TEP) and a real chemical manufacturing dataset.

在加工和制造业中，大力推动生产更高质量的产品并确保最大的加工效率，这需要有效地检测和解决干扰的方法以确保最佳操作。虽然控制系统可以补偿许多类型的干扰，但它无法处理某些较大的过程中断。因此，开发监控系统以有效检测和识别那些故障，以便操作员可以快速解决这些故障非常重要。本文提出了一种新的概率故障检测和识别方法，该方法采用了一种新开发的深度学习方法，该方法使用具有变差辍学的贝叶斯递归神经网络（BRNN）。BRNN模型是通用模型，可以对复杂的非线性动力学建模。此外，与传统的基于统计的数据驱动的故障检测和识别方法相比，基于BRNN的方法提出了不确定性估计值，可以同时进行化学过程故障检测，直接故障识别和故障传播分析。在基准田纳西·伊士曼过程（TEP）和真实的化学制造数据集中，对该方法的性能进行了演示，并与在行业中广泛应用的（动态）主成分分析进行了对比。