Rolling bearing fault diagnosis method based on FSC-MPE and BP neural network
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摘要:
目的 为了从强背景噪声、非平稳、非线性的复杂设备滚动轴承早期冲击故障振动信号中有效提取故障特征并进行故障模式识别， 方法 首先利用快速谱相关分析对原始振动信号的故障特征进行提取，并利用多尺度排列熵对故障特征进行量化，然后将故障特征数据输入BP神经网络进行故障诊断模型训练与测试，最后，对变速情况下的滚动轴承故障模拟实验数据和美国凯斯西储大学的公开轴承故障试验数据集进行故障识别研究。 结果 结果显示：所提方法对不同类型的故障具有较高的辨识精度，可达97%以上。 结论 研究验证了基于FSC-MPE与BP神经网络的滚动轴承故障诊断方法的可行性和优越性，可为滚动轴承健康状态评估提供技术支持。 Abstract:objective In order to effectively extract fault features and identify fault patterns from early impact vibration signals of rolling bearings of complex equipment with strong background noise, non-stationary and nonlinear, methods first, the fault features of the original vibration signals are extracted by fast spectral correlation analysis, and the fault features are quantified by multi-scale permutation entropy. Then the fault characteristic data are input into BP neural network for fault diagnosis model training and testing. Finally, the fault identification research is carried out on the rolling bearing fault simulation experimental data under variable speed and the public bearing fault test data set of case Western Reserve University. results the results show that the proposed method has high identification accuracy for different types of faults, which can reach more than 97%. conclusion the feasibility and superiority of rolling bearing fault diagnosis method based on FSC-MPE and BP neural network are verified, and it can provide technical support for rolling bearing health evaluation. -
图 9 试验轴承振动信号增强包络谱
Figure 9. Enhanced envelope spectrum of vibration signal of test bearing
图 10 神经网络交叉熵损失与测试集识别准确率
Figure 10. Accuracy of neural network cross entropy loss and test set recognition
表 1 轴承数据样本统计
Table 1. Sample statistics of bearing data
故障类型 损伤尺寸/in 样本数量/个 标签 正常情况 150 0 0.07 150 1 内圈故障 0.14 150 2 0.21 150 3 0.07 150 4 外圈故障 0.14 150 6 0.21 150 7 总计 1 050 
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表 3 轴承故障诊断结果
Table 3. Diagnosis results of bearing fault
故障类型 损伤尺寸/in 正确数/总数 准确率/% 正常情况 25/25 100 0.07 24/25 96 内圈故障 0.14 24/25 96 0.21 25/25 100 0.07 24/25 96 外圈故障 0.14 24/25 96 0.21 25/25 100 总计 171/175 97.71
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