Compound fault diagnosis for industrial robots based on dual-transformer networks


The accurate diagnosis of the compound fault of industrial robots can be highly beneficial to maintenance management. In the actual noisy working environment of industrial robots, the mixed and feeble failure features are easy to be overwhelmed, which poses a major challenge for the industrial robot compound fault diagnosis. Meanwhile, in the existing studies, a large-size deep learning model is the guarantee of decent denoising and fault diagnosis performance. However, this demands expensive computational costs and large data samples, which are not always available. In order to address both challenges, in this study, an integrated approach that contains two compact Transformer networks is proposed to achieve accurate compound fault diagnosis for industrial robots. In this approach, the feedback current signals collected from a six-axis industrial robot are first transformed into time-frequency image representation via continuous wavelet transformation (CWT). Secondly, a novel deep learning algorithm called compact Uformer is proposed to denoise the time-frequency image. Subsequently, the denoised time-frequency images are fed into compact convolutional Transformer (CCT) for compound fault diagnosis. An experimental study based on a real-world industrial robot compound fault dataset was conducted. The experimental results reveal that the proposed method can achieve satisfactory compound fault diagnosis accuracy based on the data collected from the noisy environment in comparison with the state-of-the-art algorithms.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > Smart and Sustainable Manufacturing
Additional Information: Copyright © 2022, The Society of Manufacturing Engineers. Published by Elsevier Ltd. This accepted manuscript version is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License []. Funding & Acknowledgements: The authors acknowledge support from multiple funds in China, including the Key Program of NSFC-Guangdong Joint Funds (U1801263), the Natural Science Foundation of Guangdong Province (2020B1515120010), Dedicated Fund for Promoting High-Quality Economic Development in Guangdong Province (Marine Economic Development Project) GDNRC [2021]44, Industrial Core And Key Technology Plan of ZhuHai City (ZH22044702190034HJL). The authors' work is also supported by Guangdong Provincial Key Laboratory of Cyber-Physical System (2020B1212060069).
Uncontrolled Keywords: Compound fault diagnosis,Deep learning,Industrial robot,Signal denoising,Transformer network,Control and Systems Engineering,Software,Hardware and Architecture,Industrial and Manufacturing Engineering
Publication ISSN: 1878-6642
Last Modified: 20 Jun 2024 07:19
Date Deposited: 04 Jan 2023 15:33
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Related URLs: https://www.sci ... 278612522002254 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-02
Published Online Date: 2022-12-19
Accepted Date: 2022-12-09
Authors: Chen, Chong
Liu, Chao (ORCID Profile 0000-0001-7261-3832)
Wang, Tao
Zhang, Ao
Wu, Wenhao
Cheng, Lianglun

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