Canonical variate analysis-based contributions for fault identification

• Canonical variate analysis-based methods are proposed for fault identification.
• Variable contributions are defined based on state space and residual space.
• A faulty variable can mostly impact the state space, the residual space, or both.
• Faulty variables were observed to be more likely associated with residual space.

While canonical variate analysis (CVA) has been used as a dimensionality reduction technique to take into account serial correlations in the process data with system dynamics, its effectiveness in fault identification (i.e., identification of variables most closely associated with a fault) in industrial processes has not been extensively investigated. This paper proposes CVA-based contributions for fault identification, where two types of contributions are developed based on the variations in the canonical state space and in the residual space. The two contributions are used to categorize faulty variables into state-space faulty variables (SSFVs) and residual-space faulty variables (RSFVs), which enhances the understanding of the character of each fault as well as the performance of fault monitoring based on different statistics. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process. The simulation results show that the faulty variables identified by the CVA-based contributions can impact the statistics of the state space, the residual space, or both; and abnormal events are observed to be more often linked to faulty variables in the residual space rather than in the state space.