LiteFDNet: A Lightweight Network for Current Sensor-Based Bearing Fault Diagnosis

Abstract

Bearings play a crucial role as key components in electric motors widely used in industrial settings. When defects occur in these bearings, they can lead to serious motor failures, causing significant downtime for the entire system. Therefore, prompt detection of bearing defects is essential to minimize system downtime and improve production efficiency. Previous research has mainly focused on using convolutional neural network (CNN)-based and recurrent neural network (RNN)-based models for bearing fault diagnosis. However, these models often involve high computational complexity for learning and rely on high-dimensional data, such as time series data collected from sensors or image data generated from time series data. Employing these models can result in significant delays in the fault diagnosis process, making them less suitable for real-world industrial environments. In this study, we propose an innovative approach to reduce both training and inference time by utilizing time domain features (TDF) obtained through the transformation of time series current data. The TDF provides a low-dimensional data representation that can be used to facilitate model training and inference. Additionally, the proposed model uses linear layers based on residual structure and dense connectivity structure to improve performance and reduce model complexity. To evaluate the effectiveness of our proposed method, we conducted simulations using the Paderborn University bearing dataset for training and inference. In experimental results, we show that the proposed method outperforms conventional methods in model complexity and fault diagnosis accuracy.

Keywords

• Bearing fault diagnosis
• current signals
• electric motors
• explainable AI
• lightweight model
• time domain feature