自适应动态阈值图神经网络:用于跨工况轴承故障诊断的新型深度学习框架

IF 2.1 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Machines Pub Date : 2023-12-28 DOI:10.3390/machines12010018
Linjie Zheng, Yonghua Jiang, Hongkui Jiang, Chao Tang, Weidong Jiao, Zhuoqi Shi, A. Rehman
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引用次数: 0

摘要

最近,基于深度学习的轴承故障诊断方法取得了重大成功。然而,在实际工程应用中,有限的标注数据和各种工作条件严重制约了大多数基于深度学习的故障诊断方法的广泛应用。此外,许多方法只关注样本的振幅信息,忽略了样本之间丰富的关系信息。为解决这些问题,本文提出了一种基于自适应动态阈值图神经网络(ADTGNN)的新型跨工况少发故障诊断方法。所提方法的目的是在故障仅发生几次甚至一次后快速识别故障类型。ADTGNN 中的自适应阈值计算模块(ATCM)会根据边缘置信度为每条边缘动态分配阈值,从而优化图结构,有效缓解过度平滑问题。此外,还引入了动态阈值调整策略(DTAS),随着训练迭代逐渐提高阈值,防止模型因性能不足而过早丢弃关键边。利用三个轴承数据集证明了所提出模型的有效性。实验结果表明,在跨条件轴承故障诊断方面,所提出的方法明显优于其他比较方法。
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Adaptive Dynamic Threshold Graph Neural Network: A Novel Deep Learning Framework for Cross-Condition Bearing Fault Diagnosis
Recently, bearing fault diagnosis methods based on deep learning have achieved significant success. However, in practical engineering applications, the limited labeled data and various working conditions severely constrain the widespread application of most deep-learning-based fault diagnosis methods. Additionally, many methods focus solely on the amplitude information of samples, neglecting the rich relational information between samples. To address these issues, this paper proposes a novel cross-condition few-shot fault diagnosis method based on an adaptive dynamic threshold graph neural network (ADTGNN). The aim of the proposed method is to rapidly identify fault types after they occur only a few times or even once. The adaptive threshold computation module (ATCM) in ADTGNN dynamically assigns thresholds to each edge based on edge confidence, optimizing the graph structure and effectively alleviating the over-smoothing issue. Furthermore, a dynamic threshold adjustment strategy (DTAS) is introduced to gradually increase the threshold with the training iterations, preventing the model from prematurely discarding crucial edges due to insufficient performance. The proposed model’s effectiveness is demonstrated using three bearing datasets. The experimental results indicate that the proposed approach significantly outperforms other comparison methods in cross-condition bearing fault diagnosis.
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来源期刊
Machines
Machines Multiple-
CiteScore
3.00
自引率
26.90%
发文量
1012
审稿时长
11 weeks
期刊介绍: Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.
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