基于增强健康数据图的深度学习行星齿轮箱故障检测

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Design and Engineering Pub Date : 2023-06-27 DOI:10.1093/jcde/qwad056
Taewan Hwang, J. Ha, B. Youn
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引用次数: 0

摘要

传统的基于深度学习的故障诊断方法在领域转移问题下面临挑战,因为模型会遇到与训练条件不同的工作条件。这一挑战在行星齿轮箱的诊断中尤为明显,因为它们产生的振动非常复杂,而且根据齿轮箱的系统特性,振动会发生很大的变化。为了解决这一挑战,本文提出了一种基于深度学习的行星齿轮箱故障检测方法,该方法利用增强型健康数据图(enHDMap)。虽然有一种HDMap方法可以根据齿轮啮合位置直观地表示行星齿轮箱的振动信号,但受机器运行条件的影响较大。在本研究中,进一步删除了HDMap中的领域特定特征,而增强了与故障相关的特征。采用基于自编码器的残差分析和数字图像处理技术来解决域偏移问题。通过对两个不同配置的变速箱试验台在平稳和非平稳工况下的运行情况进行研究,验证了该方法在显著域漂移问题条件下的性能。在所有12种可能的域移场景中测量验证准确性。该方法具有鲁棒的故障检测精度,在大多数情况下优于现有方法。
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Robust deep learning-based fault detection of planetary gearbox using enhanced health data map under domain shift problem
The conventional deep learning-based fault diagnosis approach faces challenges under the domain shift problem, where the model encounters different working conditions from the ones it was trained on. This challenge is particularly pronounced in the diagnosis of planetary gearboxes due to the complicated vibrations they generate, which can vary significantly based on the system characteristics of the gearbox. To solve this challenge, this paper proposes a robust deep-learning-based fault-detection approach for planetary gearboxes by utilizing an enhanced health data map (enHDMap). Although there is an HDMap method that visually expresses the vibration signal of the planetary gearbox according to the gear meshing position, it is greatly influenced by machine operating conditions. In this study, domain-specific features from the HDMap are further removed, while the fault-related features are enhanced. Autoencoder-based residual analysis and digital image-processing techniques are employed to address the domain-shift problem. The performance of the proposed method was validated under significant domain-shift problem conditions, as demonstrated by studying two gearbox test rigs with different configurations operated under stationary and non-stationary operating conditions. Validation accuracy was measured in all 12 possible domain-shift scenarios. The proposed method achieved robust fault detection accuracy, outperforming prior methods in most cases.
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来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
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