Detection of faults in electrical panels using deep learning method

In the image analysis, a big trend within the field of artificial intelligence is using the Deep Learning method, which is an upgrade of the existing neural network adaptive architecture (ANN). Deep Learning is a major new field in machine learning that encompasses a wide range of neural network architectures designed to perform various tasks. In the thermography energy sector, examples that are processed on a daily basis are sampling of active energy components, focus segmentation, and fault classification. The most popular network architecture for Deep Learning in image analysis is the convolution neural network (CNN), where traditional machine learning methods require determination and calculation, from which the algorithm training comes. Deep Learning approach captures important features as well as the appropriate weight of these attributes to make decision for new data. This paper describes a method and tool that are available to build and conduct an effective analysis of the Deep Learning Methodfor electrical components.