LSR-Net: A Lightweight and Strong Robustness Network for Bearing Fault Diagnosis in Noise Environment
Junseok Lee, Jihye Shin, Sangyong Lee, Chang-Jae Chun

TL;DR
This paper introduces LSR-Net, a lightweight neural network designed for accurate and real-time bearing fault diagnosis in noisy environments, utilizing novel denoising and efficiency modules.
Contribution
The paper presents a new lightweight network with a denoising module and efficiency shuffle block, improving noise robustness and reducing computational complexity for fault diagnosis.
Findings
LSR-Net outperforms benchmark models in noise robustness.
The model achieves lower computational complexity.
Effective real-time fault diagnosis demonstrated in noisy conditions.
Abstract
Rotating bearings play an important role in modern industries, but have a high probability of occurrence of defects because they operate at high speed, high load, and poor operating environments. Therefore, if a delay time occurs when a bearing is diagnosed with a defect, this may cause economic loss and loss of life. Moreover, since the vibration sensor from which the signal is collected is highly affected by the operating environment and surrounding noise, accurate defect diagnosis in a noisy environment is also important. In this paper, we propose a lightweight and strong robustness network (LSR-Net) that is accurate in a noisy environment and enables real-time fault diagnosis. To this end, first, a denoising and feature enhancement module (DFEM) was designed to create a 3-channel 2D matrix by giving several nonlinearity to the feature-map that passed through the denoising module…
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Taxonomy
TopicsMachine Fault Diagnosis Techniques · Fault Detection and Control Systems · Image and Signal Denoising Methods
