Details
| Original language | English |
|---|---|
| Article number | 110609 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 200 |
| Early online date | 29 Jul 2023 |
| Publication status | Published - 1 Oct 2023 |
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.
Keywords
- Convolutional neural network (CNN), Electromechanical systems, Fault diagnosis, Graph reasoning fusion module (GRFM), Graph-guided collaborative convolutional neural network (GGCN)
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 200, 110609, 01.10.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems
AU - Xu, Yadong
AU - Ji, J. C.
AU - Ni, Qing
AU - Feng, Ke
AU - Beer, Michael
AU - Chen, Hongtian
N1 - Funding Information: This work was supported by the National Key Research and Development Program of China under Grant No. 2022YFB3402100 , and by the National Natural Science Foundation of China under Grant 52075267 .
PY - 2023/10/1
Y1 - 2023/10/1
N2 - 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.
AB - 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.
KW - Convolutional neural network (CNN)
KW - Electromechanical systems
KW - Fault diagnosis
KW - Graph reasoning fusion module (GRFM)
KW - Graph-guided collaborative convolutional neural network (GGCN)
UR - http://www.scopus.com/inward/record.url?scp=85166262556&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2023.110609
DO - 10.1016/j.ymssp.2023.110609
M3 - Article
AN - SCOPUS:85166262556
VL - 200
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 110609
ER -