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.
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