Dconformer

A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults

verfasst von

Sheng Li, J. C. Ji, Yadong Xu, Ke Feng, Ke Zhang, Jingchun Feng, Michael Beer, Qing Ni, Yuling Wang

Abstract

Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Hohai University
University of Technology Sydney
Nanjing University of Science and Technology
Xi'an Jiaotong University
The University of Liverpool
Tongji University

Typ

Artikel

Journal

Mechanical Systems and Signal Processing

Band

210

ISSN

0888-3270

Publikationsdatum

15.03.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Steuerungs- und Systemtechnik, Signalverarbeitung, Tief- und Ingenieurbau, Luft- und Raumfahrttechnik, Maschinenbau, Angewandte Informatik

Elektronische Version(en)

https://doi.org/10.1016/j.ymssp.2024.111142 (Zugang: Geschlossen)