Intelligent temporal synchronization enhanced cointegration framework for non-stationary industrial process monitoring
Document Type
Article
Publication Date
3-1-2026
Abstract
Ensuring safe and reliable operation constitutes a fundamental requirement in modern industrial production, which can be achieved through effective process monitoring capable of identifying abnormal deviations from normal operating behavior. However, conventional data-driven multivariate statistical process monitoring (MSPM) methods rely on the assumption that the process is stationary, which is often violated in practice due to operational changes, stochastic disturbances, and external factors. Cointegration analysis (CA) offers a promising solution for this issue but its effectiveness is limited by variable time delays (VTD) caused by sensor latency, signal transmission, and spatially distributed equipment. To address this challenge, an intelligent temporal synchronization enhanced cointegration (ITSEC) framework for non-stationary industrial process monitoring is proposed, which integrates two key components. First, a clustering assisted time lags identification strategy is developed to intelligently synchronize the relevant variables. Then, synchronized time series is applied to improve the identification of long-term equilibrium relationships extracted by CA. The effectiveness of ITSEC is validated through a numerical simulation case and two real industrial cases. Comparative results show that ITSEC achieves a superior balance between high fault detection rates and low false alarm rates, proving its effectiveness and potential for practical deployment in complex industrial environments.
Publication Source (Journal or Book title)
Process Safety and Environmental Protection
Recommended Citation
Rao, J., Ji, C., Territo, K., Wang, J., Sun, W., & Romagnoli, J. (2026). Intelligent temporal synchronization enhanced cointegration framework for non-stationary industrial process monitoring. Process Safety and Environmental Protection, 208 https://doi.org/10.1016/j.psep.2026.108410