<![CDATA[Fault detection in hybrid control systems (HCS) poses significant challenges due to dynamic variations in system dynamics caused by event-based inputs and the existence of unknown large process noise. A novel scheme for optimized robust fault detection of HCS has been proposed and projected here that can effectively handle dynamic system changes and process noise along with the fault detection while achieving high accuracy and reliability. The challenge with the HCS is the presence of a large process noises due to changing of state equations drastically with dynamical input making the fault detection a complex task. The derivative-free estimator minimizes process noise and provides reliable state estimation, while the Markov Decision Process (MDP) framework is employed to optimize fault detection. MDP has been chosen here due to its mathematical introspection for dynamic system's decision-making process when the results are random or under the control of a decision maker. The data generated by the derivative-free estimator is used to train this deep learning model. Simulation studies were conducted to evaluate the scheme’s performance, and additional tests for convergence, optimization, and robustness were performed using MDP infused with adaptive estimators. The efficacy of the proposed estimators has been confirmed on a benchmark problems, namely the liquid level control system for an chemical stirred tank reactor (CSTR) model. Simulation studies has been employed to prove the efficacy of the proposed method. The proposed method achieved 98.6% fault detection accuracy and a 12% mean error reduction compared to existing techniques. It demonstrated robustness under varying noise levels, dynamic conditions and presence of external disturbances . The results confirm the method's effectiveness for robust and optimized fault detection in HCS, offering a scalable, accurate, and noise-resilient solution for real-world industrial systems.]]>
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