Data-Driven Health Status Assessment of Fire Protection IoT Devices in Converter Stations

To enhance fire safety in converter stations, this study focuses on detecting abnormal data and potential faults in fire protection Internet of Things (IoT) devices, which are networked sensors monitoring parameters such as temperature, smoke, and water tank levels. A data quality evaluation model is proposed, covering both validity and timeliness. For validity assessment, a transformer-based time series reconstruction method is used, and anomaly thresholds are determined using the peaks over threshold (POT) approach from extreme value theory. The experimental results show that this method identifies anomalies in fire telemetry data more accurately than traditional models. Based on the objective evaluation method and clustering, an interpretable health assessment model is developed. Compared with conventional distance-based approaches, the proposed method better captures differences between features and more effectively evaluates the reliability of fire protection systems. This work contributes to improving early fire risk detection and building more reliable fire monitoring and emergency response systems.