Infrared faults recognition for electrical equipments based on dual supervision signals deep learning

In order to improve the accuracy of infrared fault image recognition for electrical equipment, an infrared fault image recognition method for electrical equipment based on double supervised signal deep learning was proposed. Firstly, a Slic super pixel segmentation algorithm was adopted to merge the similar pixel regions into blocks. According to the luminance information provided by the improved HSV space transformation, the temperature abnormal regions were determined. Secondly, the connected areas and the corresponding device of this region were separated. Finally, based on the GoogLeNet convolution neural network model, fault features of infrared images for electrical equipments were extracted, then trained and supervised by two kinds of signals, i.e., the softmax loss and the center loss signal. Among an established 700 infrared fault of electrical equipment images dataset, 500 of which are for training, and 200 for testing. Experiments results show that the test accuracy rate can reach to 98.6% which enhanced 1% when being compared with the classic method simply using the single softmax loss. The algorithm can accurately locate five kinds of electrical equipments which include the transformer bushing, current transformer, surge arrester, isolating switch, insulators, as well as identify the corresponding faults.