A Direct Transfer Entropy-Based Multiblock Bayesian Network for Root Cause Diagnosis of Process Faults

Abstract

In chemical processes, Bayesian network (BN)-based approaches have been extensively applied for process fault diagnosis. Generally, BN is learned using score and search algorithms where search algorithms create candidate networks whose fitness to data is measured by scores. However, existing approaches cannot utilize cyclic loop knowledge while learning BN. Since cyclic loops are prevalent in chemical processes, their unaccountability results in inaccurate BN and reduces diagnosis accuracy. Therefore, for accurate diagnosis, we propose direct transfer entropy (DTE)-based multiblock BN to discover cyclic loops while learning BN. First, the process is segmented into multiple blocks. Next, block-level BNs are learned using DTE-based score and Greedy search. By eliminating the common source variable effect, DTE finds correct causality between process variables and obtains accurate block-level BNs, which are fused to discover significant cyclic loops that were otherwise unachievable when finding BN. The performance of the developed methodology is demonstrated through a benchmark process.

Transfer Entropy-Based Automated Fault Traversal and Root Cause Identification in Complex Nonlinear Industrial Processes

“…The Tennessee Eastman process (TEP) is widely used as an industrial benchmark for time series data analysis, and fault detection, and diagnosis. − The process flow diagram of TEP is shown in Figure . The plant consists of 5 major process units: an exothermic two-phase reactor, a product stripper, a condenser, a vapor–liquid separator, and a recycle compressor (Downs & Vogel, 1993).…”

Data-Knowledge-Driven Modeling and Operational Adjustment for the Pharmaceutical Tablet Manufacturing Process via Wet Granulation

“…In recent years, data-knowledge-driven approaches have attracted increasing attention and interest in academia and have achieved the broad applications in solving various engineering problems such as system modeling and control, process monitoring, fault diagnosis, and so on. − Specifically, as an important artificial intelligence (AI) technique, the Bayesian network (BN) is a type of probabilistic graphical model that is capable of effectively integrating data and knowledge to simulate human reasoning. It represents the causal relations of variables by a directed acyclic graph, and so it has better interpretability than other AI methods. , Owing to the advantages in interpretability, probabilistic modeling, and dealing with data uncertainties, BNs have been widely applied to a variety of industrial systems and processes in different areas to successfully solve problems such as process monitoring, fault diagnosis, prognosis, risk assessment, decision making, etc. ,, However, the BN-based PQC studies in the pharmaceutical field are rarely reported. Because of its significant advantages and application potential, we attempt to utilize BN for PQC (or operational adjustment) tasks for the first time.…”

“… 24 , 25 Owing to the advantages in interpretability, probabilistic modeling, and dealing with data uncertainties, BNs have been widely applied to a variety of industrial systems and processes in different areas to successfully solve problems such as process monitoring, fault diagnosis, prognosis, risk assessment, decision making, etc. 26 , 27 , 30 However, the BN-based PQC studies in the pharmaceutical field are rarely reported. Because of its significant advantages and application potential, we attempt to utilize BN for PQC (or operational adjustment) tasks for the first time.…”