A model predictive early warning system for disturbance type faults

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Fault detection and diagnosis (FDD) systems are an integral part of modern process plants. Traditionally, FDD systems generate an alarm only after the actual signal or the processed signal has crossed the threshold. Though this is a robust approach, in some cases it may be too late to make any amendments to the system. The goal of this research program is to incorporate predictive features in FDD systems for early warning, to give operators sufficient time to take corrective actions. In the proposed research, we focus on disturbance-type faults. Process systems are prone to disturbances: some of them are measured while others are unmeasured. Disturbances perturb process systems gradually; the effect of a disturbance can be predicted before its impact is fully felt in the process. We propose a new framework for FDD that will estimate an unknown disturbance entering into a process system, predict its impact on the system, and finally issue an alarm if the impact is deemed significant. The proposed predictive fault detection and diagnosis framework consists of two modules: (i) simultaneous input and state estimation (SISE) module, and (ii) a moving horizon prediction and feasibility analysis module. Disturbances entering process systems, as well as other states within the system, are usually unmeasured. The SISE module will estimate both the unknown states and the disturbance entering the system. An expectation maximization (EM) algorithm will be used to iteratively estimate the states and the unknown disturbance magnitude. The EM algorithm has excellent statistical properties and guarantees convergence. Based on the estimated disturbance magnitude, system model, and available measurements, the future states of the system will be predicted using a moving horizon predictor. Finally, feasibility analysis will be carried out to determine if there is sufficient capacity in the actuators to counteract the disturbance effects and keep the outputs within the operational constraints. An alarm will be issued if there is no feasible solution. The novel FDD system is expected to issue an alarm earlier than the traditional alarm systems. It will make process industries, as well as many other industries, safer and improve the efficiency of operation by providing early indications of faults. Through this research program, two PhD and two Master's students will be trained. They will gain knowledge on the theory and applications of process monitoring and control and hands-on training on industry standard distributed control systems (DCS) and supervisory control and data acquisition (SCADA) system. These are highly sought after skills in Canadian industries. Thus, the proposed research program will contribute significantly to Canadian economy.