Root cause diagnosis of quality-related faults in industrial multimode processes using robust Gaussian mixture model and transfer entropy

Modern complex industrial processes often have multiple operating modes due to various factors, such as different manufacturing strategies, alterations of feedstock and compositions, etc. In this paper, a practical technology or solution of quality-related fault diagnosis is put forward for industrial multimode processes. Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis. The new scheme addresses the quality-related fault detection issue with a developed robust Gaussian mixture model and modified Mahalanobis distance. Then, a Bayesian inference-based robust Gaussian mixture contribution index is designed to analyze the potential root-cause variables. Meanwhile, a combination of transfer entropy and direct transfer entropy-based cause and effect extraction methodologies is proposed for root cause diagnosis of quality-related faults. Finally, the whole proposed framework is applied to a real industrial multimode finishing mill process, where the performance and effectiveness are further demonstrated from real industrial data.