Abstract
The NVH (Noise, Vibration, Harshness) performance of electric vehicle reducer gears directly affects the NVH level of the whole vehicle. However, the existing single gear quality detection methods based on tooth surface waviness are faced with two major challenges, which are unable to detect quickly and cannot fully characterize the real performance. This work introduces the first real industrial dataset for manufacturing quality inspection of electric vehicle reducer gears to solve these two challenges and provide data benchmarks. This dataset covers the dynamic meshing transmission data of five types of manufacturing offline gears (healthy gears, slight bump gears, leaky grinding gears, and two kinds of ghost order whine gears). And a variety of uncertain factors in the manufacturing process (different machine tools, different batches, different device control parameters, etc.) are introduced to avoid the influence of laboratory ideal conditions on signal characteristics. The data quality of this dataset was evaluated using a 1D-CNN classifier and benchmarked against WTGCM, MCC5-THU, and AGFD, where it exhibited superior performance.
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DMTD can be accessed unrestrictedly in the ScienceDB repository via the following link: https://doi.org/10.57760/sciencedb.27983.
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The model code used in this work can be accessed through the following link:
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Acknowledgements
This work was supported by the Innovation and Development Joint Fund of Chongqing Natural Science Foundation (grant No. CSTB2022NSCQ-LZX0051); and the National Natural Science Foundation of China (grant No. 52572402). The authors express their gratitude to the U.S. Department of Energy (DOE)/National Renewable Energy Laboratory (NREL), Tsinghua University, and Aalto University for providing the datasets that facilitated this study.
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Supervision, D.G., Y.X., M.L.; Writing – Original Draft, J.Y.; Writing – Review & Editing, D.G. J.Y.; Data Curation, Y.H.; Validation, H.L., X.L.
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Guo, D., Yang, J., Li, H. et al. A dynamic meshing transmission dataset for manufacturing quality inspection of electric vehicle reducer gears. Sci Data (2026). https://doi.org/10.1038/s41597-026-06885-1
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DOI: https://doi.org/10.1038/s41597-026-06885-1
