A Rolling Bearing Fault Diagnosis Method Based on Time-frequency Analysis and Residual Neural Networks

CHEN Rui; SHA Yundong

doi:10.7643/issn.1672-9242.2026.09.013

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Equipment Environmental Engineering ›› 2026, Vol. 23 ›› Issue (9) : 115-124. DOI: 10.7643/issn.1672-9242.2026.09.013

Special Topic—Structural Dynamics of Aviation Equipment

A Rolling Bearing Fault Diagnosis Method Based on Time-frequency Analysis and Residual Neural Networks

CHEN Rui, SHA Yundong^*

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Abstract

The work aims to propose a hybrid method combining the constant Q transform (CQT) with a residual neural network (ResNet 18) to address the challenges associated with fault feature extraction from rolling bearing vibration signals, namely the difficulty in identifying fault characteristics and the susceptibility of these signals to being masked by noise. First, the original vibration signals were processed using the constant Q transform to generate a time-frequency representation. Feature enhancement was then applied to this time-frequency representation employing the overlapping sampling method and limited contrast adaptive histogram equalization. After batch processing, a dataset was constructed and divided into a training set and a test set. Next, a ResNet 18 network was trained on the partitioned training set; the trained model was subsequently used to process and analyze the test set, thereby establishing a rolling bearing fault diagnosis model that integrated the constant Q transform and the residual neural network. A dedicated experimental platform for extracting typical rolling bearing fault features was developed. The effectiveness of this hybrid rolling bearing fault diagnosis model was validated using publicly available dataset; the proposed method was then applied to the in-house experimental platform. Under the operating conditions and dataset specified in this study, effective fault diagnosis was achieved for the rolling bearings, with the recognition accuracy, precision, recall, and F1-score for the test set all reaching 100%. In conclusion, the rolling bearing fault diagnosis method based on the constant Q transform and the residual neural network described in this paper can serve as an effective approach for fault diagnosis and feature recognition in rolling bearings across various mechanical systems.

Key words

rolling bearing / fault diagnosis / constant Q transform / residual neural network / deep learning

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CHEN Rui, SHA Yundong. A Rolling Bearing Fault Diagnosis Method Based on Time-frequency Analysis and Residual Neural Networks[J]. Equipment Environmental Engineering. 2026, 23(9): 115-124 https://doi.org/10.7643/issn.1672-9242.2026.09.013

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