Smart equipment failure detection with machine learning applied to thermography inspection data in modern power systems

This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel processing to improve accurateness is highlighted in the methodology. The proposed method is capable of identifying early warning signs of equipment failure and predicting when the failure is likely to occur. The proposed approach demonstrates the potential for early detection of equipment failure in modern power systems with accurate clustering. The use of machine learning algorithms applied to thermography inspection data provides a reliable and effective way to identify and predict equipment failures, ultimately leading to improved system reliability and reduced maintenance costs.