A Holistic Probabilistic Framework for Monitoring Nonstationary Dynamic Industrial Processes

Multivariate statistical process monitoring (MSPM) methods provide sensitive indicators of process conditions by harnessing the value of massive process data. Large-scale industrial processes are subject to wide-range time-varying operating conditions such that some variables inevitably exhibit nonstationary behavior, which poses significant challenges for the design of MSPM schemes. In this brief, a novel nonstationary probabilistic slow feature analysis algorithm is developed to comprehensively describe both nonstationary and stationary variations that underlie process measurements during routine operations. For efficient parameter estimation, the expectation-maximization algorithm is employed. By modeling nonstationarity and stationarity as the random walk and stable autoregressive processes, interpretable monitoring statistics are constructed to detect abnormality in nonstationary dynamics, stationary dynamics, and stationary steady conditions. This forms a holistic and pragmatic monitoring framework for industrial processes, which is beneficial for reducing false alarms and providing meaningful operational information for industrial practitioners. The efficacy of the proposed monitoring framework is validated via two case studies.

Automated summary

Multivariate statistical process monitoring methods provide sensitive indicators of process conditions by utilizing large amounts of process data. A novel nonstationary probabilistic slow feature analysis algorithm is developed to comprehensively describe nonstationary and stationary variations, with the expectation-maximization algorithm used for efficient parameter estimation. Interpretable monitoring statistics are constructed to detect abnormalities in nonstationary and stationary dynamics, forming a holistic and pragmatic monitoring framework for industrial processes.