Fault diagnosis of planetary gearbox based on multi-information fusion and convolutional neural network
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摘要: 基于机器学习的行星齿轮箱故障诊断方法依赖人工选择特征向量，而特征向量选择的优劣很大程度上决定了诊断方法的准确率。卷积神经网络（CNN）能自动提取特征，但用于行星齿轮箱故障诊断时难以通过单一振动信号对故障做出精确诊断。针对上述问题，提出了一种基于多信息融合和CNN的行星齿轮箱故障诊断方法。对行星齿轮箱的三向（水平径向、垂直径向与轴向）振动信号和声音信号进行数据层融合，将一维的振动信号和声音信号通过并联方式整合为一个二维信号；将二维信号作为CNN的输入，利用多个卷积层和最大池化层进行深度特征提取和信息过滤，最终通过Softmax分类器实现故障分类。搭建了行星齿轮箱故障诊断实验台，采集不同转速和负载工况下行星齿轮箱正常和故障状态的振动信号和声音信号，并输入CNN中进行训练和验证。在相同条件下选取水平径向振动信号、垂直径向振动信号、轴向振动信号、声音信号4种单源信息分别与CNN相结合的方法进行对比，以验证基于多信息融合和CNN的行星齿轮箱故障诊断方法的优越性，实验结果表明：轴向振动信号+CNN和声音信号+CNN 2种方法的故障识别准确率分别为74.07%和75.13%；水平径向振动信号+CNN和垂直径向振动信号+CNN 2种方法的故障识别准确率分别为89.70%和87.09%；基于多信息融合和CNN方法的收敛速度最快，故障识别准确率最高，为93.33%。Abstract: The fault diagnosis method of planetary gearbox based on machine learning relies on the artificial selection of the eigenvectors. The quality of eigenvectors selection largely determines the accuracy of the diagnosis method. The convolutional neural network (CNN) can extract characteristics automatically. But it is difficult to accurately diagnose the fault from a single vibration signal when it is used for the planetary gearbox fault diagnosis. To solve the above problems, a fault diagnosis method of planetary gearbox based on multi-information fusion and CNN is proposed. The method performs data layer fusion on three-dimensional (horizontal radial direction, vertical radial direction and axial direction) vibration signals and sound signals of the planetary gearbox. The one-dimensional vibration signals and sound signals are integrated into two-dimensional signals in a parallel connection mode. The two-dimensional signals are used as the input of CNN. The multiple convolutional layers and maximum pooling layers are used for depth characteristic extraction and information filtering. Finally, the Softmax classifier is used to achieve fault classification. The fault diagnosis experiment platform of the planetary gearbox is built. The vibration signals and sound signals of normal and fault states of the planetary gearbox under different speed and load conditions are collected and input into CNN for training and verification. Four single-source information of horizontal radial vibration signal, vertical radial vibration signal, axial vibration signal and sound signal are selected under the same conditions and combined with CNN respectively for comparison. The experiment is used to verify the superiority of the fault diagnosis method for planetary gearbox based on multi-information fusion and CNN. The experimental results show that the fault identification accuracy of the two methods of axial vibration signal+CNN and sound signal+CNN is 74.07% and 75.13% respectively. The fault identification accuracy of the two methods of horizontal radial vibration signal+CNN and vertical radial vibration signal+CNN is 89.70% and 87.09% respectively. The method based on multi-information fusion and CNN has the fastest convergence speed and the highest fault identification accuracy, which is 93.33%.
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图 4 行星齿轮箱不同状态的振动时域波形
Figure 4. Vibration time domain waveform of planetary gearbox in different status
图 5 基于多信息融合和CNN的故障识别准确率
Figure 5. Fault identification accuracy based on multi-information fusion and CNN
图 6 基于单源信息和CNN的故障识别准确率
Figure 6. Fault identification accuracy based on single source information and CNN
表 1 CNN参数
Table 1. Parameters of CNN
层类型 核尺寸 步幅 核数量 卷积层1 2×7 （1，1） 32 最大池化层1 1×4 （1，4） — 卷积层2 2×7 （1，1） 64 最大池化层2 1×4 （1，4） — 卷积层3 2×7 （1，1） 128 最大池化层3 1×4 （1，4） — 
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表 2 数据集
Table 2. Data set
行星齿轮箱状态 样本数 标签 正常状态 600 0 太阳轮点蚀 600 1 太阳轮断齿 600 2 齿圈断齿 600 3 行星轮磨损 600 4 轴承外圈故障 600 5 轴承内圈故障 600 6 轴承滚珠故障 600 7 轴承保持架故障 600 8
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表 3 不同方法的故障识别准确率
Table 3. Fault identification accuracy of different methods
诊断方法 测试集准确率/% 多信息融合+CNN 93.33 水平径向振动信号+CNN 89.70 垂直径向振动信号+CNN 87.09 轴向振动信号+CNN 74.07 声音信号+CNN 75.13
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