Identification of asynchronous motor and transformer situations in thermal images by utilizing transfer learning-based deep learning architectures

Abstract

Asynchronous motors are thoroughly preferred in industrial applications regarding their advantages in com-parison with other motor types, and transformers constitute another oft-used category for the adjustment of voltage to be fed to an electrical system. Concerning the inevitable usage of these equipment, the fault di-agnostics are generally fulfilled by in-depth determinations of stator current signals, magnetic flux distributions, etc. which require conventional electrical measurements. Herein, thermal image analyses arise as an easy way to identify the situations of electrical equipment in which there is no need for direct intervention to the structure. In this paper, we handle the thermal image-based analyses to distinguish the situations of asynchronous motors and transformers. For this purpose, without a pre-processing step, efficient deep learning architectures (Dense-Net201, MobileNetV2, ResNet50, ShuffleNet, Xception) are examined to discriminate twenty situations formed by combining the conditions of both types of equipment. These conditions are defined as the cooling fan failure, rotor fault, short-circuit faults in different phases and in various rates inside the motor, short-circuit faults in different rates inside the transformer, no-load motor, and no-load transformer. In experiments, the hyper-parameters of models are examined in a comprehensive manner to observe the highest scores of architectures that can be achieved. Herein, four phenomena (mini-batch size, learning rate, learning rate drop factor, opti-mizer type) are evaluated to perform the transfer learning task, not to spoil the main part of the models, and to reveal the appropriate model for thermal image classification. In trials, an 80-20% training-test split is allowed to compare the models without data augmentation. As a result, the highest performance is observed with all deep learning architectures by attaining 100% accuracy for condition discrepancy of thermal images belonging to the three phase-motor and one phase-transformer. In addition to the accuracy-based analyses, an in-depth evaluation is presented to reveal the most appropriate architecture in thermal image classification.