<p>Standby diesel generators (SDGs) in nuclear power plants are important in maintaining continuous power supply during disturbance in the grid; hence, their reliability is paramount in the safety of nuclear power plants. The complex electromechanical, thermal, and fluid interactions that are required to accurately predict abnormal operating conditions can only be predicted using an integrated approach. Traditional protection and control methods usually work with simplified models and threshold-based diagnostics, which do not necessarily identify faults at an early stage. Such a weakness may result in slow response and accessibility of SDGs in the emergency situation. It presents a Cascaded Integrated Multiphysics Fusion Diagnosis (C-IMFD) method that jointly employs high-fidelity multiphysics simulation and data-driven feature fusion. The technique combines 3D thermal fluid models, nonlinear electrical dynamics and vibration-based structural representations. A hierarchical learning module is a hybrid feature embedding, which integrates probabilistic fault inference to detect, categorize, and localize incipient faults in real-time. Simulation and experimental validation show 23–40% improvement in the accuracy of fault detection early. An increase in the rate of anomaly localization and increase in the resistance to sensor noise over conventional model-only or data-only systems. C-IMFD framework is a potent tool to improve the protection and control performance of SDGs, as a dependable system of decision support to nuclear emergency power systems. The proposed method achieves the fault detection accuracy of 88–93%, fault localization precision (88–92%), anomaly detection latency of 120&#xa0;ms, FAR (6–7%), and fault classification accuracy of 92%.</p>