Industrial Fault Detection Using Transfer Learning Models

Abstract

Industry and equipment are critical factors in the advancement of human society in the era of the industrial revolution. Since factories are reliant on their machines, they must be maintained daily. However, if the machines are too large for us to observe, an automated process is required to monitor it. By diagnosing the signal data using the CNN algorithm, faults in the machines can be identified. This paper has proposed three transfer learning-based fault diagnosis models using AlexNet, InceptionV3, GoogLeNet with the pretrained weights of the ImageNet dataset. The results of the classification of the three models are compared for their performance. It is observed from the study that the proposed AlexNet architecture shows a very high performance by classifying faults in machines for the tested dataset compared to other models.