Research on Bearing Vibration Signal Analysis Method Combining Vibration Image and CNN
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摘要: 本文以强背景噪声下振动信号特征提取和建模分析为研究目标，提出将振动信号转换为振动图像的信号变换方法，以深沟球轴承故障诊断振动信号和轴承质量等级评估振动信号为实验数据集，基于振动图像的卷积神经网络模型（VI-CNN），并采用正确识别率（CRR）作为模型精度的评价指标。实验结果表明：对于轴承故障诊断和质量等级评估的定性判别，采用VI-CNN对比其他建模方法正确识别率分别为100%和98.16%，模型有更好的稳健性。Abstract: The paper takes the vibration signal feature extraction and modeling analysis under strong background noise as the research objective, and proposes a signal transformation method to convert the vibration signal into a vibration image. The experimental data setincludes the vibration signal for deep groove ball bearing fault diagnosis and the vibration signal for bearing quality evaluation,based onconvolutional neural network model (VI-CNN) of vibration images, and the correct recognition rate (CRR) is used as an evaluation index of model accuracy. The experimental results show that for the qualitative discrimination of bearing fault diagnosis and quality level evaluation, the correct recognition rates of VI-CNNmodel compared with other modeling methods are 100% and 98.16%, indicating the model has better robustness.
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Key words:
- bearings /
- vibration signal /
- fault diagnosis /
- quality assessment /
- vibration image
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表 2 轴承故障识别结果统计
方法 训练集平均识别率/% 测试集平均识别率/% VS-SVM 100.00 ± 0 95.62 ± 1.50 VSTF-SVM 98.82 ± 0.40 97.25 ± 2.15 VI+HOG+SVM 100.00 ± 0 99.62 ± 0.80 VI-CNN 100.00 ± 0 100.00 ± 0 
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表 3 轴承质量等级评估结果统计
方法 训练集平均识别率/% 测试集平均识别率/% VS-SVM 100.00 ± 0 84.83 ± 4.43 VSTF-SVM 97.77 ± 1.40 86.66 ± 3.33 VI-HOG-SVM 100.00 ± 0 95.83 ± 2.49 VI-CNN 100.00 ± 0 98.16 ± 0.49
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