State estimation is a fundamental component of model-based Fault Detection and Diagnosis (FDD) in dynamic systems, underpinning real-time monitoring, predictive maintenance, and safety-critical operations across industries such as aerospace, power systems, robotics, and autonomous vehicles. Traditional estimators, including the Kalman Filter (KF) and its variants, provide physically interpretable residuals for fault detection but often fail to deliver reliable performance under nonlinear dynamics, modeling uncertainties, sensor faults, and non-Gaussian noise. This paper presents a comprehensive review of state estimation-based FDD approaches, with a particular focus on Artificial Intelligence (AI)-augmented Kalman filtering and hybrid frameworks that integrate Machine Learning (ML) models, including Neural Networks (NNs), Support Vector Machines (SVMs), and Gaussian Processes (GPs), with classical estimation theory. The review systematically evaluates model-based, data-driven, and hybrid methods, comparing their robustness, accuracy, computational efficiency, scalability, and interpretability in complex Cyber-Physical Systems (CPSs). Furthermore, emerging trends and open research challenges are identified, including online adaptation, fault-tolerant estimation, sensor fusion, explainable artificial intelligence (XAI), and deployment in Industry 4.0 and Internet of Things (IoT)-enabled environments. By bridging classical estimation theory with modern AI techniques, this review provides a roadmap for designing intelligent, adaptive, and resilient FDD systems capable of enhancing reliability, operational safety, and real-world applicability.
Sahar Gargouri, Majdi Mansouri, Ahmed Anis Kahloul et al.· Energies· 0 citations
Renewable energy systems, including solar photovoltaic arrays and wind turbines, operate under highly variable environmental and operating conditions. Factors such as changing irradiance, temperature fluctuations, wind variability, and component aging make Fault Detection and Diagnosis (FDD) particularly challenging. Therefore, developing reliable, accurate, and interpretable diagnostic methods is essential to ensure system efficiency, safety, and long-term operation. Traditional model-based approaches, which rely on physical system models, offer clear interpretability and solid theoretical foundations. However, their effectiveness can be limited by modeling inaccuracies and difficulties in capturing complex nonlinear behaviors. On the other hand, data-driven and Artificial Intelligence (AI) techniques have demonstrated strong capabilities in pattern recognition and fault classification, but often face challenges related to data dependence, limited transparency, and reduced robustness under unseen conditions. This paper provides a comprehensive and structured review of FDD techniques for renewable energy systems, covering model-based, signal-based, data-driven, and hybrid approaches. A unified perspective is presented to clarify the strengths, limitations, and application domains of each category. Particular attention is given to recent advances in hybrid methods that combine physical modeling and AI, including feature fusion, ensemble learning, attention-based models, and transfer learning. Moreover, advanced signal processing techniques are discussed for their role in extracting meaningful features from noisy and non-stationary data. Rather than ranking methods by headline accuracy, which has become saturated and is only weakly comparable across heterogeneous datasets, the review adopts a critical, deployment-oriented perspective that emphasizes cross-condition robustness, standardized benchmarking, and the constraints of real-world deployment. The review also highlights the growing importance of digital twin technology as a promising framework for next-generation FDD systems, enabling real-time monitoring, adaptive learning, and predictive maintenance. Furthermore, Explainable AI is explored as a key direction for improving the transparency and trustworthiness of AI-based diagnostic models. Finally, the paper identifies major challenges and open research issues, such as data scarcity, generalization among different operating conditions, computational efficiency, and system reliability. Future research directions are outlined toward developing more robust, adaptive, and interpretable FDD solutions that can operate effectively in dynamic and uncertain environments.
Marouane Marzouk, Majdi Mansouri, Ahmed Anis Kahloul et al.· IEEE Access· 1 citation
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