Fault Subspace Selection Approach Combined With Analysis of Relative Changes for Reconstruction Modeling and Multifault Diagnosis

Online fault diagnosis has been a crucial task for industrial processes, which in general is taken after some abnormalities have been detected. Reconstruction-based fault diagnosis has been drawing special attention as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific reconstruction model (i.e., fault subspace) that can well eliminate alarm signals from a bunch of alternatives that have been prepared based on historical fault data. However, in practice, the abnormality may result from the joint effects of multiple faults, which thus cannot be well corrected by single-fault subspace archived in the historical fault library. In this paper, an aggregative reconstruction-based fault diagnosis strategy is proposed to handle the case where multiple-fault causes jointly contribute to the abnormal process behaviors. First, fault subspaces are extracted based on historical fault data in two different monitoring subspaces where analysis of relative changes is taken to enclose the major fault effects that are responsible for different alarm monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature that will sort and select the informative fault subspaces by evaluating their significances in data correction. Finally, an aggregative fault subspace is calculated by combining the selected fault subspaces, which represents the joint effects from multiple faults and works as the final reconstruction model for online fault diagnosis. Theoretical support is framed and the related statistical characteristics are analyzed. Its feasibility and performance are illustrated with simulated multiple faults from the Tennessee Eastman benchmark process.