Observer-based diagnosis and predictive framework for sensor-fault tolerant control of process systems

Sensors are ubiquitous in modern industrial systems, and are prone to faults due to harsh condition in which they are placed. Sensor faults can impact the product quality and operational safety as control loops heavily depends on the accuracy of sensor measurement feedback. In this paper, we propose active sensor-fault tolerant control (FTC) strategies that can take proactive measures during faults involving timely correction of faulty measurements, ensuring the system remains within predefined safety and quality constraints. The concept of maximal output admissible set is leveraged to determine acceptable operating set (AOS) which is the set of initial process states that meet safety and quality constraints at all times. To support decision-making, we introduce a novel critical fault function (CFF) that quantifies the fault size and time available before the system exits the AOS if no corrective action is taken. While the AOS and CFF are computed offline, the CFF is implemented online with real-time fault estimates to trigger measurement correction in time. A linear functional observer and a nonlinear state observer, combined with a predictive scheme is proposed to estimate fault size and enhance robustness during transient phase of observers. Alternatively, a bank of state observers is used for fault detection and isolation and subsequently the state observer estimator based on healthy sensors are utilized for state feedback in control loops. The proposed sensor FTC strategy is tested on an exothermic Continuous Stirred Tank Reactor (CSTR) as a case study. The results demonstrate the strategy's effectiveness in handling sensor faults, ensuring both quality and safety constraints are met. Thus, this paper contributes to the advancement of practical active sensor FTC ensuring the resilience of industrial systems. • A FTC algorithm is developed to maintain quality and safety constraints in the presence of sensor faults. • Proposes a predictive fault estimation scheme to estimate fault magnitude, enabling proactive control actions. • Proposes an alternative observer-bank approach for fault detection, isolation, and signal reconstruction.