Abstract:

Process safety has always been one of the most important issues in the chemical industry, fault detection and diagnosis(FDD) is one of the most powerful tools for chemical process abnormal events management, which provides guarantee for process safety. With the development of deep learning, many intelligent learning algorithms have been proposed, but these algorithms are rarely applied to FDD. In this paper, a novel chemical process fault diagnosis method is proposed based on sparse filtering and logistic regression (SFLR) .The main idea of this method is to divide chemical process raw data into training data and test data firstly, then standardized and whitening preprocessing, and then train sparse filtering(SF) model with three layers of neural networks, and use the SF model for unsupervised feature learning. Finally, logistic regression(LR) model with supervised learning is used to classify the chemical process health conditions using learned features. The proposed method was verified by the TE process and the non-catalytic oxidation of cyclohexane to cyclohexanone. The results show that the proposed method has high diagnostic accuracy, and the case study shows that the proposed method can diagnose faults in a timely and effective manner.

Key words: fault detection and diagnosis, safety, sparse filtering, logistic regression, neural networks, process systems

摘要：

过程安全一直以来是化学工业中尤为重要的问题之一，故障检测与诊断（FDD）作为化工异常工况管理最有力的工具之一，给过程安全提供了保障。随着深度学习的发展，很多智能学习算法已经被提出，然而这些算法却很少被应用到FDD中来。提出了一种基于稀疏过滤和逻辑回归（SFLR）算法的化工过程故障检测新方法。采用TE过程和环己烷无催化氧化制环己酮过程对提出的方法进行了验证，结果表明，所提出的方法均具有较高的诊断精度，案例研究表明提出的方法可以及时有效地诊断出故障。

关键词: 故障检测与诊断, 安全, 稀疏过滤, 逻辑回归, 神经网络, 过程系统