Faulty bearing diagnostic model based on multi-dimensional signal and multi-analysis domain

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-08-21 DOI:10.1007/s00202-024-02522-5
Shuo Wang, Bokai Guang, Zihao Wang, Xiaohua Bao
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引用次数: 0

Abstract

Deep learning and multidimensional signal fusion are utilized to fully extract fault features and integrate them into effective signals to cope with special cases in bearing fault diagnosis. Current mainstream data fusion methods only utilize vibration signals, and the vast majority of signal analysis is limited to the time domain. In addition, in the mainstream data fusion scheme, the fusion capability of the signal collector is relatively low, and the correlation and compatibility between the data cannot be guaranteed. In order to further improve the judging ability of signal features, this paper proposes a bearing fault diagnosis model based on multi-dimensional signals and multi-analysis domain. In this model, a multi-dimensional signal data model with multiple analysis domains is used for feature extraction and fusion. And the independent networks are classified according to their functions, and a single network is used to establish a data feature fusion system, while other networks extract features from different sensors. To ensure the fusion of signal acquisition from different analysis domains, multiple fusion nodes are added between the layers of the fusion network and an attention mechanism is introduced to self-weight the different features. Through experiments, technical comparisons were conducted to improve the efficiency of feature recognition and the accuracy of defect classification, and to verify the effectiveness and feasibility of the proposed method.

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基于多维信号和多分析域的故障轴承诊断模型
利用深度学习和多维信号融合技术充分提取故障特征,并将其整合为有效信号,以应对轴承故障诊断中的特殊情况。目前主流的数据融合方法仅利用振动信号,绝大多数信号分析仅限于时域。此外,在主流数据融合方案中,信号采集器的融合能力相对较低,数据之间的相关性和兼容性无法得到保证。为了进一步提高信号特征的判断能力,本文提出了一种基于多维信号和多分析域的轴承故障诊断模型。在该模型中,采用多分析域的多维信号数据模型进行特征提取和融合。并根据独立网络的功能进行分类,利用单一网络建立数据特征融合系统,其他网络则从不同传感器中提取特征。为确保不同分析领域信号采集的融合,在融合网络的层与层之间增加了多个融合节点,并引入了关注机制对不同特征进行自加权。通过实验进行技术比较,提高了特征识别的效率和缺陷分类的准确性,验证了所提方法的有效性和可行性。
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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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