A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems

authored by

Yadong Xu, J. C. Ji, Qing Ni, Ke Feng, Michael Beer, Hongtian Chen

Abstract

Collaborative fault diagnosis has become a hot research topic in fault detection and identification, greatly benefiting from emerging multisensory fusion techniques and newly developed convolutional neural network (CNN) models. Existing CNN models take advantage of various fusion techniques to identify machine health status by utilizing multiple sensory signals. Nevertheless, a few of them are able to simultaneously explore modality-specific features and intrinsic shared features among multi-source signals, limiting the capability of the exploration of multisource data. To address this issue, this paper proposes a novel convolutional network called a graph-guided collaborative convolutional neural network (GGCN) for highly-effective fault diagnosis of electromechanical systems. The main contributions of this study include: (1) developing a novel graph-guided CNN algorithm for collaborative fault detection; (2) establishing a graph reasoning fusion module (GRFM) to explore the inherent correlations between multisource signals; and (3) advancing the current approaches by taking into account both the distribution gap and the intrinsic correlation between different signals simultaneously. The developed GGCN is expected to shed new light on collaborative fault diagnosis using the graph-convolution-based intermediate fusion scheme. Two experimental datasets namely, the cylindrical rolling bearing dataset and the planetary gearbox dataset, are applied in this paper to verify the efficacy of the GGCN. Experimental results demonstrate that GGCN outperforms seven other state-of-the-art approaches, particularly under noisy conditions.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

Nanjing University of Science and Technology
UTS University of Technology Sydney
National University of Singapore
University of Liverpool
Tongji University
Shanghai Jiaotong University

Type

Article

Journal

Mechanical Systems and Signal Processing

Volume

200

ISSN

0888-3270

Publication date

01.10.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Control and Systems Engineering, Signal Processing, Civil and Structural Engineering, Aerospace Engineering, Mechanical Engineering, Computer Science Applications

Electronic version(s)

https://doi.org/10.1016/j.ymssp.2023.110609 (Access: Closed)