Single Model-Based Analysis of Relative Causal Changes for Root-Cause Diagnosis in Complex Industrial Processes

Abstract

Traditional root-cause diagnosis methods analyze different faults independently, suffering from repetitive modeling efforts for each fault. Besides, they are disturbed by significant redundancy and interference relations, being both time and effort consuming in online applications. In this work, a novel strategy is proposed to diagnose root cause by analyzing the changes in causal effects between the normal status and fault statuses, which allows a single causal model to diagnose different faults. The enhanced cross mapping (ECM) is proposed to carry out the analysis of the causal effects, which could detect precise causality and handle interference relations in complex industrial processes. In our strategy, a causal model is constructed from coarse-grained to fine-grained via the ECM to remove interference relations and achieve a concise structure. Besides, causal intensity and direction information of normal status are comprehensively described in the model. By updating causal effects under fault statuses, the relative causal changes from normal to fault can be analyzed and the significant changes are considered responsible for the fault propagation. Consequently, the fault propagation path is highlighted from the causal model and the root cause can be identified along the path. The feasibility of the proposed strategy is demonstrated using both simulation and practical industrial case studies.

A Novel Fault Diagnosis Framework for Industrial Production Processes Based on Causal Network Inference

“…The statistical independence and invariance properties indicated by the causal graphical models are used for extracting primary and secondary features of variables, which can significantly eliminate the smearing effect. Tian and Zhao 17 proposed a new strategy for analyzing the causal relationships between the normal and fault states by the enhanced cross-mapping approach, which can effectively deal with the interdependence in complex industrial processes. Zhang et al 18 proposed a calculus-based individual average causal effect estimation to reveal causal relationships between faults; a backward structural causal model is then constructed for fault detection and diagnosis according to the discovered causal relationships.…”

Variable Time Delay-Based Granger Causality Approach Integrated with Dynamic Coupling Analysis for Root Cause Diagnosis in Chemical Processes

“…In order to solve the two interference terms of the synchronization phenomenon and the Moran effect, improved CCM has been proposed to overcome these limitations to remove redundancy and interference relations [25]. Recently, a new method ECM based on CCM was designed to remove redundancy and interference relations [26]. These methods compensate for the disadvantage that GC analysis cannot be used for weak to medium coupling.…”