Abstract
To address the challenges of high structural noise, unstable operating conditions, and susceptibility to single-channel failure in multi-sensor monitoring of industrial robot transmission components, this paper proposes a cross-machines domain generalized fault diagnosis method based on fault intrinsic representation and channel self-healing. This method aims to extract essential fault characteristics from distinctive public datasets and transfer them to complex industrial robot target scenarios. First, considering the characteristics of single-point multi-directional monitoring of robot joints, a dual-channel vibration feature extraction framework is constructed. Information enhancement and splicing are used to generate intra-domain and cross-domain joint features. Combined with a set-level class-prototype regularized mechanism, the distribution differences specific to the operating conditions are decoupled and eliminated from multi-source domain data, extracting the common fault intrinsic representations across datasets. Second, to solve the monitoring blind spot problem caused by single sensor failure, a semantically supervised channel self-heal module is designed. This module uses the feature distribution of intact channels to deduce the semantic information of missing channels, achieving signal self-healing and complementation during the testing phase. Finally, rigorous cross-machines transfer experiments are designed using three public datasets containing basic fault characteristics as source domains. Zero-shot tests are conducted on the gearbox dataset characterized by high structural noise and the bearing dataset featuring extreme non-stationary start-stop conditions. Experimental results demonstrate that the proposed method effectively overcomes cross-machines distribution shifts and maintains high-precision fault diagnosis, even under extreme conditions of complete single-channel sensor failure, verifying the feasibility and robustness of transferring laboratory data to complex robot application scenarios.
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Data availability
The datasets used for graphs and table are available from the corresponding author upon request.
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Funding
This work was supported by the Natural Science Research Project of Shanxi Province (202203021211284) and the Science and Technology Innovation Project for Higher Education Institutions in Shanxi Province (Grant No.2022L611).
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Zhiguang Yao conceived the study, designed the methodology, supervised the research, and revised the manuscript. Haocheng Zhao implemented the model, conducted experiments, and analyzed the results. Yanhui Wang curated the datasets and prepared the figures and tables. Chenghao Xu assisted in module design, result analysis, and manuscript editing. All authors reviewed and approved the final manuscript.
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Yao, Z., Zhao, H., Wang, Y. et al. Industrial robot transmission components cross-machines fault diagnosis via fault intrinsic representation and channel self-healing under sensor failure. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47066-9
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DOI: https://doi.org/10.1038/s41598-026-47066-9
