Background: The increasing complexity and interconnectedness of cyber-physical systems (CPS)—including multi-phase electric drives, unmanned aerial vehicles, underwater systems, building automation networks, and high-performance computing devices—demand robust approaches for the detection, isolation, and mitigation of faults. Traditional centralized diagnosis and single-method strategies face scalability, adaptability, and reliability challenges when applied to heterogeneous deployments that combine mechanical, electrical, and software components. This work synthesizes theoretical perspectives and applied developments from decentralized estimation, machine learning, sliding-mode observers, and health-aware control to propose an integrated, publication-ready framework for resilient fault diagnosis and fault-tolerant control across CPS domains.

Objectives: The goals are threefold: (1) to review and unify multiple methodologies for fault detection, isolation, and tolerant control drawn from recent literature; (2) to propose a structured methodology that harmonizes decentralized/distributed estimation with modern data-driven classifiers and domain adaptation techniques; and (3) to demonstrate, through descriptive analysis, how the hybrid framework addresses practical constraints—such as limited observability, actuator heterogeneity, and online transferability—highlighting performance tradeoffs and deployment pathways.

Methods: The proposed methodology builds on modular elements: multiple-model estimation for decentralized and distributed settings, residual generation and multiple-valued evaluation of residuals, sliding-mode observers for fault identification, and a spectrum of supervised and deep learning approaches for classification and localization of faults. Emphasis is placed on domain adaptation for online detection in nonstationary environments, actuator reliability modelling for health-aware control, and rule-based auto-correction mechanisms used in building management systems. The methodology describes algorithmic interfaces, data-flow patterns among modules, and practical considerations for implementation.

Results: Through descriptive analysis grounded in the literature, the framework demonstrates theoretical robustness in isolating both abrupt and incipient faults across representative applications: multi-phase power electronics, electric motors with trapezoidal back-EMF, multi-rotor UAV actuators, underwater thruster systems, bearings and rotating machinery, and building HVAC networks. The hybrid approach yields complementary advantages: decentralized multiple-model estimators provide scalability and local fault isolation (Straka & Punčochář, 2020); sliding-mode observers offer model-based precision for actuator faults (Zuev et al., 2020); data-driven classifiers and deep transfer methods provide adaptability to unknown operating regimes and noisy measurements (Mao et al., 2021; Li et al., 2021).

Conclusions: A synthesis of model-based and data-driven fault diagnosis with distributed estimation and health-aware control offers a pragmatic route to enhanced resilience in CPS. Challenges remain in standardized benchmarking, interpretability, and safe automatic correction, but the integrated pathway provides clear practical steps for engineering adoption across industries. The work concludes with a detailed discussion of limitations, deployment recommendations, and prioritized future research directions.

Fault diagnosis, fault-tolerant control, distributed estimation, machine learning

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