Conformal Anomaly Detection for Predictive Maintenance in Thermal Power Plants

Thermal power plants are essential for maintaining a stable electricity supply but face high operational costs and potential economic losses due to unexpected equipment failures. Predictive maintenance (PM) aims to reduce these risks by detecting early anomalies, but existing anomaly detection methods lack statistical control over the false positive rate (FPR), leading to frequent false alarms and inefficient maintenance interventions. We hypothesize that a conformal anomaly detection (CAD)-based approach, incorporating calibration-conditional p-value adjustments, can effectively mitigate the issue of uncontrolled FPR by ensuring statistically valid anomaly detection for power plant PM, even when multiple dependent hypothesis tests are performed on operational data. We introduce a CAD framework for power plant PM, which integrates calibration-conditional validity adjustments to systematically control FPR while maintaining sensitivity to operational faults. The evaluation results of the proposed CAD framework for PM in thermal power plants show that it ensures statistically valid FPR control, significantly reducing false alarms while maintaining high sensitivity to faults and strong discriminative ability between normal and faulty operations. By providing automatic and statistically valid FPR control without manual threshold tuning, the proposed framework offers a robust and scalable solution for anomaly detection in thermal power plant PM, improving maintenance efficiency and reducing unnecessary interventions.