MARNet:用于噪声条件下滚动轴承故障诊断的多头注意力残差网络

IF 1.8 4区 工程技术 Q3 ENGINEERING, MECHANICAL
Linfeng Deng, Guojun Wang, Cheng Zhao, Yuanwen Zhang
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引用次数: 0

摘要

滚动轴承是旋转机械的关键部件,其健康状态直接影响机械的整体性能。因此,检测和诊断轴承故障极为必要。许多轴承故障诊断方法已被成功用于确保旋转机械的安全运行。然而,在实际工作环境中,存在大量噪声,导致传统方法无法实现准确的故障诊断。本文提出了一种新的多头注意力残差网络(MARNet),用于噪声条件下的滚动轴承故障诊断。MARNet 通过将多层卷积简化为单层卷积来优化残差单元,并用指数线性单元(ELU)函数取代整流线性单元(ReLU)函数,从而获得更合适的激活函数。此外,在残差块中引入了多头注意机制,以捕捉任意两个时间序列之间的相关信息,从而增强网络的特征提取能力。通过对凯斯西储大学(CWRU)和帕德博恩大学(PU)的两个轴承数据集进行实验,证明了 MARNet 在噪声环境中的有效性和优越性。实验结果表明,与几种最新的滚动轴承故障诊断深度学习方法相比,所提出的方法具有抗噪声特性和泛化能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MARNet: Multi-head attention residual network for rolling bearing fault diagnosis under noisy condition
Rolling bearings are crucial components of rotating machinery, and their health states directly affect the overall performance of the machinery. Therefore, it is exceedingly necessary to detect and diagnose bearing faults. Numerous bearing fault diagnosis methods have been successfully used for ensuring the safe operation of rotating machinery. However, in practical working environments, there is a considerable amount of noise, resulting in traditional methods incapable of achieving accurate fault diagnosis. This paper proposes a new multi-head attention residual network (MARNet) for rolling bearing fault diagnosis under noisy condition. MARNet optimizes residual units by simplifying multi-layer convolutions into a single-layer convolution and replaces the rectified linear unit (ReLU) function with the exponential linear unit (ELU) function to obtain a more appropriate activation function. Additionally, the multi-head attention mechanism is introduced into the residual block to capture correlation information between any two time sequences, enhancing the network’s feature extraction capability. The effectiveness and superiority of the MARNet in noisy environments are demonstrated through conducting the two bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU). The experiment results show that the proposed method exhibits anti-noise characteristics and generalization capability compared with several up-to-date deep learning methods for fault diagnosis of rolling bearings.
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来源期刊
CiteScore
3.80
自引率
10.00%
发文量
625
审稿时长
4.3 months
期刊介绍: The Journal of Mechanical Engineering Science advances the understanding of both the fundamentals of engineering science and its application to the solution of challenges and problems in engineering.
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