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国外文献 > 1.轴承 > 5. 监测与诊断 > 状态监测

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标 题： A Fault Diagnosis Approach Based on 2D-Vibration Imaging for Bearing Faults
基于二维振动成像的轴承故障诊断方法
作 者： Mishra R. K.
Choudhary Anurag
Fatima S.
Mohanty A. R.
Panigrahi B. K.
关键词： Vibration analysis  [振动分析]
Signal-to-image conversion  [ 信号与图像转换]
Time-domain analysis  [ 时域分析]
STFT  [ STFT]
CNN  [ CNN]
SVM  [ 支持向量机]
出版年： 2023
期刊名： Journal of Vibration Engineering & Technologies
振动工程与技术杂志
年卷期： 2023, vol.11, no.7
摘 要： Background The widely used rolling element bearings in rotating machines undergo progressive degradation with continuous operation. To identify bearing faults, complex time-frequency based signal processing techniques and high-end deep neural network algorithms have been used to perform fault classification, which is time-consuming.Method In this paper, the focus was given to replace the complex time-frequency domain signal processing techniques by incorporating a simple time-domain based methodology. Initially, the vibration signature of different bearing faults was acquired at three different speeds and was directly converted into images by 2D-Vibration Imaging (2D-VI) technique using an overlapping-based moving window. The extracted images were fed into Convolutional Neural Network (CNN) for automatic feature extraction, followed by classification using Support Vector Machine (SVM).Results and validation Separately, time-frequency spectrums were also extracted to compare the effectiveness of the proposed methodology. Furthermore, the proposed methodology was validated on the bearing dataset of combined faults and Case Western Reserve University (CWRU).Conclusion The experimental results showed that the proposed methodology has the potential to replace the conventional approach by consuming less computational time without affecting classification accuracy.

背景：旋转机械中广泛使用的滚动元件轴承在持续运行过程中会逐渐发生退化。为了识别轴承故障，人们采用复杂的时频信号处理技术和先进的深度神经网络算法进行故障分类，但这些方法耗时较长。 方法：本文重点在于用一种简单的时域方法替代复杂的时频域信号处理技术。首先，在三种不同转速下获取不同轴承故障的振动特征，并通过基于重叠移动窗口的二维振动成像（2D-VI）技术直接转换为图像。提取的图像输入卷积神经网络（CNN）进行自动特征提取，随后使用支持向量机（SVM）进行分类。 结果与验证：此外，还单独提取了时频谱以比较所提方法的有效性。同时，该方法在包含多种故障类型的轴承数据集以及Case Western Reserve大学（CWRU）的数据集上进行了验证。结论 实验结果表明，所提出的方法在不影响分类准确性的前提下，能够以更少的计算时间取代传统方法。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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