用多尺度频谱图+CNN提升轴承故障诊断准确率
Bearing fault diagnosis based on multi-scale spectral images and convolutional neural network
- 通过多长度傅里叶变换生成多尺度频谱图
- 在两个实验中诊断准确率显著提升
- 适合需要高精度振动故障识别的工程场景
为解决传统轴承故障诊断方法诊断准确率低的问题，本文提出一种基于多尺度谱特征图像与深度学习的新型故障诊断方法。首先对振动信号进行均值去除预处理，再通过快速傅里叶变换（FFT）转换为多长度频谱；其次，采用多长度频谱铺排方案构建一种新型特征——多尺度谱图像（MSSI）；最后，建立卷积神经网络（CNN）深度学习框架实现故障诊断。通过两个实验案例验证了该方法的有效性。实验结果表明，所提方法显著提升了故障诊断准确率。
原文摘要 · Abstract (English)
To address the challenges of low diagnostic accuracy in traditional bearing fault diagnosis methods, this paper proposes a novel fault diagnosis approach based on multi-scale spectrum feature images and deep learning. Firstly, the vibration signal are preprocessed through mean removal and then converted to multi-length spectrum with fast Fourier transforms (FFT). Secondly, a novel feature called multi-scale spectral image (MSSI) is constructed by multi-length spectrum paving scheme. Finally, a deep learning framework, convolutional neural network (CNN), is formulated to diagnose the bearing faults. Two experimental cases are utilized to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method significantly improves the accuracy of fault diagnosis.
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