Anomaly detection in irregular image sequences for concentrated solar power plants

. Operations at extremely high temperatures can lead to various malfunctions in Concentrated Solar Power (CSP) plants, emphasizing the need for predictive maintenance (PdM). We study PdM as an anomaly detection (AD) problem from irregular image sequences, which represent the minute-by-minute solar receiver’s surface temperature from a CSP plant. Contrary to standard benchmark image datasets in AD research, our data shows distinct characteristics such as non-stationarity, temporal dependence, and irregular sampling, which are unaddressed by current image-based AD techniques. Therefore, we introduce a forecast-based AD method to address these characteristics, drawing inspiration from irregular sequence modelling. The results show that the proposed method outperforms classical image-based AD methods on our dataset.