Fault subspace selection and analysis of relative changes based reconstruction modeling for multi-fault diagnosis

Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific fault subspace that can well eliminate alarming 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 can not be well corrected by single fault subspace archived in the historical fault library. In the present work, 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 alarming monitoring statistics. Then, a fault subspace selection strategy is developed to analyze the combinatorial fault nature which will sort and select the informative fault subspaces that are most likely to be responsible for the concerned abnormalities. 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 multi-faults using data from the Tennessee Eastman (TE) benchmark process.