Work

Lightweight physical information diffusion model for fault diagnosis of rolling bearings with imbalanced sample

Year: 2025

Type: article

Abstract: Intelligent fault diagnosis of rolling bearings under data imbalance remains a critical challenge in industrial environments. A lightweight physical information diffusion model (LPIDM) is proposed to address the scarcity and imbalanced distribution of fault samples. Firstly, a region-adaptive noise schedule is introduced to replace the conventional linear schedule, enabling targeted augmentation of fault-relevant regions. Secondly, a depthwise separable residual (DSR) structure is incorporated into the U-Net architecture to reduce model complexity and number of parameters. Finally, a multi-objective, collaborative optimization loss function is designed to improve time–frequency fidelity of generated signals. The performance of the proposed method is evaluated through experiments on public and laboratory bearing datasets. The results demonstrate that LPIDM can generate high-quality fault samples, improve diagnostic accuracy and effectiveness under imbalanced conditions, and offer a practical solution for intelligent fault diagnosis in actual industrial scenarios.

Source: Measurement Science and Technology

DOI: 10.1088/1361-6501/ae1316

Authors: Wanjie Du, Changfeng Yan, Bin Liu, Yuan Huang, Jianxiong Kang, Lixiao Wu

Institution: Lanzhou University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,325