Coupling of adjoint-based Markov/CCMT predictive analytics with data assimilation for real-time risk scenario forecasting of industrial digital process control systems

Abstract

Risk early detection and controls are critical to avoid unforeseen accidents or incidents across safety-critical industries especially in a digitalization context. Using the sequential Monte Carlo particle filter method, an adjoint-based Markov/Cell-to-Cell Mapping Technique (Markov/CCMT) predictive analytics coupling with data assimilation is proposed in the paper for dynamic risk and reliability scenario modeling of industrial digital process control systems. The particle filter-based data assimilation method is developed with a sequential importance sampling with resampling scheme for extended system state estimation and dynamic reshaping of system state-space model. Then the Markov/CCMT predictive analytics models are dynamically constructed using an equal-weight quadrature scheme for approximating the probabilistic cell-to-cell mapping relations that reflect the possible system state transitions on the discretized state-space model in real time. The adjoint-based Markov/CCMT modeling for risk scenario forecasting is demonstrated on a digital U-shaped Tube Steam Generator (UTSG) water level control system in nuclear power plants. The demonstration results show that super real-time and consistent predictions can be obtained within the adjoint-based Markov/CCMT predictive analytics framework. Multi-step time-series forecasting towards the identification of system-level degradation states and risk-significant scenarios evolution can also be envisioned when a blended state merging and pruning strategy is adopted in deep model search. The proposed Markov/CCMT predictive analytics is able to deliver proactive insights to intelligent operation and maintenance of complex safety-critical engineering systems.