利用分数傅里叶变换信息融合和轻量级神经网络对滚动轴承故障进行早期诊断

IF 3.6 2区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Fengyun Xie, Gang Li, Chengjie Song, Minghua Song
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引用次数: 0

摘要

为了应对轴承故障早期诊断中与特征提取和诊断模型复杂性相关的挑战,本文提出了一种用于滚动轴承早期故障诊断的创新方法。该方法结合了频域信号分析和轻量级神经网络的概念。首先,利用振动传感器采集滚动轴承的振动信号,并将均方值作为精确提取早期故障信号的指标。随后,利用分数傅里叶变换将时域信号转换为频域信号,从而提供更详细的频率特性信息。融合过程结合了幅频和相频信息,并可视化为克角场图。轻量级神经网络 Xception 被选为主要的故障诊断工具。Xception 是一种卷积神经网络(CNN)变体,其轻量级设计可在大幅减少模型参数的同时保持出色的性能,因此被选中。实验结果表明,Xception 模型在滚动轴承故障诊断方面表现出色,尤其是在利用融合信息数据集时。这一结果凸显了信息融合与 Xception 模型相结合以提高早期滚动轴承故障诊断准确性的优势,并为工业环境中的健康监测和故障诊断提供了可行的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network
In response to challenges associated with feature extraction and diagnostic models’ complexity in the early diagnosis of bearings’ faults, this paper presents an innovative approach for the early fault diagnosis of rolling bearings. This method combined concepts from frequency domain signal analysis with lightweight neural networks. To begin, vibration signals from rolling bearings were collected using vibration sensors, and the mean square value was utilized as an indicator for accurate early fault signal extraction. Subsequently, employing the fractional Fourier transform, the time domain signal was converted into a frequency domain signal, which provided more detailed frequency feature information. The fusion process combined amplitude frequency and phase frequency information, and was visualized as a Gram angle field map. The lightweight neural network Xception was selected as the primary fault diagnosis tool. Xception, a convolutional neural network (CNN) variant, was chosen for its lightweight design, which maintains excellent performance while significantly reducing model parameters. The experimental results demonstrated that the Xception model excelled in rolling bearing fault diagnosis, particularly when utilizing fused information datasets. This outcome underscores the advantages of combining information fusion and the Xception model to enhance the accuracy of early rolling bearing fault diagnosis, and offers a viable solution for health monitoring and fault diagnosis in industrial settings.
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来源期刊
Fractal and Fractional
Fractal and Fractional MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.60
自引率
18.50%
发文量
632
审稿时长
11 weeks
期刊介绍: Fractal and Fractional is an international, scientific, peer-reviewed, open access journal that focuses on the study of fractals and fractional calculus, as well as their applications across various fields of science and engineering. It is published monthly online by MDPI and offers a cutting-edge platform for research papers, reviews, and short notes in this specialized area. The journal, identified by ISSN 2504-3110, encourages scientists to submit their experimental and theoretical findings in great detail, with no limits on the length of manuscripts to ensure reproducibility. A key objective is to facilitate the publication of detailed research, including experimental procedures and calculations. "Fractal and Fractional" also stands out for its unique offerings: it warmly welcomes manuscripts related to research proposals and innovative ideas, and allows for the deposition of electronic files containing detailed calculations and experimental protocols as supplementary material.
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