Causation-Based T² Decomposition for Multivariate Process Monitoring and Diagnosis

Abstract

Multivariate process monitoring and diagnosis is an important and challenging issue. The widely adopted Hotelling T² control chart can effectively detect a change in a system but is not capable of diagnosing the root causes of the change. The MTY approach makes efforts to improve the diagnosability by decomposing the T² statistic. However, this approach is computationally intensive and has a limited capability in root-cause diagnosis for a large dimension of variables. This paper proposes a causation-based T² decomposition method that integrates the causal relationships revealed by a Bayesian network with the traditional MTY approach. Theoretical analysis and simulation studies demonstrate that the proposed method substantially reduces the computational complexity and enhances the diagnosability, compared with the MTY approach.

• Bayesian Network
• Causal Model
• SPC

Additional information

Notes on contributors

Jing Li

Dr. Li is an Assistant Professor in the Department of Industrial Engineering at Arizona State University. She is a Member of ASQ. Her email address is [email protected].

Jionghua Jin

Dr. Jin is an Associate Professor in the Department of Industrial and Operations Engineering at the University of Michigan. She is a Member of ASQ. Her email address is [email protected].

Jianjun Shi

Dr. Shi is a Professor in the Department of Industrial and Operations Engineering at the University of Michigan. He is a Senior Member of ASQ. His email address is [email protected].