An Explainable Convolutional Neural Network for Fault Diagnosis in Linear Motion Guide
Min Su Kim, Jong Pil Yun, PooGyeon Park
Abstract
A linear motion (LM) guide is a mechanical tool for requiring linear motion in a system. Repeating linear movements can cause cracking and deterioration of the LM guide, which can lead to a decrease in productivity. Therefore, predicting the status of the LM guide and diagnosing faults are essential for systems including the LM guide. In this article, we propose a novel framework of fault diagnosis model based on deep learning using a vibration sensor signal mounted on the LM guide. This framework contains the learning vibration signal in the time domain using the proposed 1-D convolutional neural network model and the visualization of the classification criteria in the frequency domain using the learned model in the time domain. To utilize the visualization in the frequency domain, the proposed model is designed to maintain the frequency information in the learning process. With the learned model, we propose a frequency domain-based grad-CAM to visualize the classification criteria in the frequency domain to help to explain the characteristics of normal and fault data. Using LM guide data under various conditions, we visualize the classification criteria of the learned model in the frequency domain.