<![CDATA[Power station engines are critical infrastructure components that require constant monitoring to prevent failures and ensure an uninterrupted power supply. This paper proposes a failure early warning system based on a thermal camera using a computer vision approach. The system uses a thermal camera to generate thermal images in a video format, which is then processed by an automated fire detection engine and temperature detection engine. The results of these two subsystems are then used as input for an anomaly detection engine, which predicts the likelihood of engine failure. Based on the results of the experiments, it can be concluded that the YOLOv7 model outperforms Faster R-CNN in detecting fires, achieving a higher mAP score on the one-class dataset. The proposed temperature and anomaly detection system also accurately detected temperature levels and anomalies in thermal images. Furthermore, in the failure time prediction experiment, the Holt-Winters additive method with additive errors, additive trend, and additive seasonality model was identified as the best fit among the models evaluated. In contrast, the Decision Tree model showed good performance and a short training time, making it a good choice for applications where training time is critical. These results highlight the importance of selecting the most suitable method for a given application. Moreover, it demonstrates the effectiveness of different models and approaches for engine failure early warning systems in a power station using a thermal camera.]]>
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