Online fault diagnosis and state identification during process transitions using dynamic locus analysis

Chemical plants operate in a variety of states; some of these are steady states while others including grade changes, startup, shutdown, and maintenance operations are transitions. Transition operations are usually challenging and more prone to abnormalities. Therefore, automated process monitoring during transitions is important. In this paper, we propose a new signal comparison-based approach, called dynamic locus analysis, for online state identification and fault diagnosis during process transitions. Dynamic locus analysis is an extension of Smith and Waterman's [1981. Identification of common molecular subsequence. Journal of Molecular Biology 147, 195–197] discrete sequence comparison algorithm to continuous signals. It uses dynamic programming to efficiently identify the portion of a long reference signal that best matches another signal. During online application, signals from real-time sensors are compared with those from prior process runs to identify the current process state as well as estimate its progress. Run-to-run variations between the reference and online signals are accounted for by using dynamic time warping (DTW) for signal comparison. Dynamic locus analysis can be directly used for multivariate temporal signals and has the computational efficiency needed for real-time application. Extensive testing on three case studies—the Tennessee Eastman challenge problem, a lab-scale distillation column, and a simulated fluidized catalytic cracking unit—reveal that the proposed method can quickly identify normal as well as abnormal process states.