A robust multi-scale learning network with quasi-hyperbolic momentum-based Adam optimizer for bearing intelligent fault diagnosis under sample imbalance scenarios and strong noise environment

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Maoyou Ye, Xiaoan Yan, Ning Chen, Ying Liu
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引用次数: 0

Abstract

Due to adverse working conditions of rotating machinery in actual engineering, bearing fault data are more difficult to acquire compared to normal data. That said, the real collected bearing vibration data are usually characterized by imbalance. Meanwhile, fault information of the raw collected bearing vibration data is effortlessly drowned out by strong noises, which indicates that it is awkward to efficiently recognize bearing fault states via using traditional fault diagnosis methods under this background. To overcome these problems, this research proposes an individual approach formally intituled as robust multi-scale learning network (RMSLN) with quasi-hyperbolic momentum-based Adam (QHAdam) optimizer for bearing fault diagnosis, which mainly includes convolution-pooling operation, multi-scale branch, and classification layer. Within the proposed method, the channel attention mechanism based on squeezed excitation network is embedded into the multi-scale branch in the form of residual connections, which not only reinforce important information and weaken noise interference, but also capture fault features more comprehensively and enhance the discrimination of fault states with fewer samples. Additionally, in the training process, QHAdam optimizer is introduced to tightly control the loss of RMSLN to enable a faster and smoother convergence. Two groups of experimental bearing data are studied to support the availability of presented approach, and several traditional fault diagnosis methods and representative imbalance fault diagnosis approaches are compared in four evaluation metrics (accuracy, macro-precision, macro-recall, and macro-F1 score) to highlight the advantages of the presented method.
基于Adam优化器的准双曲动量鲁棒多尺度学习网络在样本不平衡和强噪声环境下的轴承智能故障诊断
由于实际工程中旋转机械的工作条件恶劣,与正常数据相比,轴承故障数据更难获取。也就是说,实际收集的轴承振动数据通常具有不平衡的特征。同时,原始采集的轴承振动数据的故障信息很容易被强噪声淹没,这表明在这种背景下,使用传统的故障诊断方法很难有效地识别轴承的故障状态。为了克服这些问题,本研究提出了一种用于轴承故障诊断的独立方法,正式命名为鲁棒多尺度学习网络(RMSLN)和基于拟双曲动量的Adam(QHAdam)优化器,该方法主要包括卷积池运算、多尺度分支和分类层。在所提出的方法中,基于压缩激励网络的通道注意机制以残差连接的形式嵌入到多尺度分支中,不仅增强了重要信息,削弱了噪声干扰,而且可以更全面地捕捉故障特征,增强了对故障状态的识别能力。此外,在训练过程中,引入了QHAD优化器来严格控制RMSLN的损失,以实现更快、更平滑的收敛。研究了两组轴承实验数据,以支持所提出方法的可用性,并在四个评估指标(准确性、宏精度、宏召回率和宏-F1分数)上对几种传统故障诊断方法和具有代表性的不平衡故障诊断方法进行了比较,以突出所提出的方法的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
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