ThermalFault: A Session-Disjoint Dataset and Benchmark for Online Motor Fault Monitoring

Thermal imaging provides a contact-free method for monitoring motor condition. While thermal images can be evaluated individually and effectively, most applications require capturing the event structure needed for online monitoring and/or the correlation of the adjacent thermal frames. This prior information is useful for assessing false alarms, detection delay, post-detection diagnosis, signal recovery, among others. We introduce ThermalFault, an event-annotated thermal dataset and benchmark for motor fault monitoring. The dataset contains 21,556 frames from 18 development sessions covering normal operation, fan faults, single-phase undervoltage, and motor operation at 40 and 50 Hz. Three held-out mixed sessions with fault and recovery periods are included for external evaluation. ThermalFault defines six tasks: frame classification, normal-only anomaly detection, online event detection, post-trigger fault diagnosis, recovery monitoring, and external stress testing. We examine the proposed dataset using classical, deep-learning, time-series, and pretrained vision baselines. We further propose a two-stage pipeline for online fault detection and diagnosis. The detection stage combines deviation from a learned normal thermal trajectory with temporally smoothed fault probabilities from a supervised image classifier. A causal state machine converts the combined evidence into alarm triggers and classifies each event as either a fan fault or single-phase undervoltage. Across six grouped folds, the pipeline detects 11 of 12 fault events at 0.167 false alarms per normal monitoring hour and correctly diagnoses all 11 detected events. These results support session-disjoint, event-level, and recovery-aware evaluation for online thermal fault monitoring.