An AI-Driven Framework for Thermal Sensor Stability Assessment and Predictive Fault Diagnosis in Industrial Cooling Systems: A Comparative Study of SVM and LSTM Approaches
An AI-driven framework integrating stability feature engineering with machine learning models for fault identification and early prediction of temperature sensors in power plant cooling systems, which highlights the difficulty of learning-based prediction for rare, rule-defined thermal sensor anomalies.