Causal Neuromorphic Graph Learning with Physics-Informed Micro-Twins for Explainable Wind Turbine Fault Diagnosis

Wind Turbine Generators (WTGs) are considered one of the essential elements in the renewable energy system. Despite this, due to the complex, non-linear, coupled nature of fault dynamics, fault detection of WTG poses enough challenge. Conventional signal processing, model-based estimation, and machine learning fault diagnosis methods provide valuable insights, but have limitations in prediction, scalability, comprehensibility, and uncertainty. New hybrid frameworks relying on techniques like deep learning fused with fuzzy decision support help enhance the detection of harmonics and condition-based optimization of control devices. Unfortunately, these various advanced methods are currently missing under-utilized capabilities that focus on explainable, reasonable, and proactive prediction of the fault’s progressive harm—a paper framework for advanced fault diagnosis and predictive maintenance of Wind Turbine Generators (WTGs). To begin, the turbine’s dynamics are embedded into adaptive digital twins using physics-informed neural networks that develop self-evolving micro-digital twins at the component level (rotor, gearbox, bearings, stator). Secondly, the neuromorphic spiking Graph Neural Networks (S-GNNs) will extract temporal transients from multiple vibration, current, and rotor speed signals while integrating causal inference for root-cause analysis and counterfactual reasoning. The system combines micro-twin predictions with causal learning of neuromorphic to provide estimates of fault intensity, RUL, and interpretable decision support. This technique will provide improved accuracy of fault diagnosis, real-time deployment on edge devices using voltage-efficient neuromorphic computing, uncertainty calibration of RUL prediction, and operator trust using transparent causal explanations. Through the unification of physics-informed modeling, neuromorphic efficiency, and causal AI, this work sets the groundwork for the next generation of explainable and sustainable fault diagnosis in wind turbine generators.