This is like a smart mechanic for power-plant valve actuators: it watches sensor data, predicts when parts are likely to fail, and also explains in plain engineering terms why it thinks a failure is coming (e.g., which pressures, temperatures, or vibrations are driving the risk).
Unplanned failures of governor valve actuators in power generation equipment cause costly outages, safety risks, and inefficient maintenance schedules. Traditional AI models for predictive maintenance are often black boxes that engineers do not trust. This work uses explainable AI methods to predict failures and to show which operating conditions and features are most responsible for the prediction, improving both reliability and engineer adoption.
Domain-specific feature engineering and labeled failure/maintenance history for governor valve actuators in real operating environments, plus explainability tuned to how reliability engineers think about equipment behavior.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Collecting sufficient labeled failure/degradation events from rare faults; integration with plant historians and OT systems; compute/storage for high-frequency sensor time series and model retraining.
Early Majority
Focus on explainability for post-hoc and pseudo-post-hoc analysis tailored to governor valve actuators, enabling engineers to see which sensor signals and operating conditions drive predicted failures—beyond generic predictive-maintenance black-box models.
133 use cases in this application