Work

A Semi-supervised Fault Diagnosis Method for Rolling Bearing Using Time-Frequency Transform Enhanced Contrastive Learning

Year: 2025

Type: article

Abstract: This paper proposes a novel semi-supervised framework, time-frequency Contrastive Learning (CL), to address the challenge of accurate rolling bearing fault diagnosis under industrial small-sample conditions. Raw vibration signals are transformed into discriminative time-frequency images using short-time Fourier transform (STFT). A CL network with a ResNet18 model is pre-trained on a lot of unlabeled samples to learn generalized feature, and the ResNet18 model is fine-tuned using small labeled samples for fault classification. Experimental validation on bearing fault datasets demonstrates that the proposed STFT-CL method achieves above 99% diagnosis accuracy with only 50 labeled samples per fault type, outperforming conventional semi-supervised methods by 6-12%. The proposed method provides a potential solution to the "small sample dilemma" in industrial applications through the synergistic effect of physically driven signal processing and self supervised representation learning.

Source: Eksploatacja i Niezawodnosc - Maintenance and Reliability

DOI: 10.17531/ein/207015

Authors: Jicai Wang, Shahrum Abdullah, Azli Arifin, Salvinder Singh Karam Singh, Chen Gao

Institutions: Jiaxing Nanyang Polytechnic Institute, National University of Malaysia

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: gold

Estimated APC paid: $442