Health assessment method of traction motor bearing based on transfer learning and convolutional neural network
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摘要: 针对牵引电机轴承健康评估中带标签的全寿命周期振动数据获取与可反映轴承性能退化趋势的健康指标构建困难的问题，提出了一种基于迁移学习和卷积神经网络的牵引电机轴承健康评估方法；采用迁移学习，以带标签的轴承全寿命周期数据集为源域数据，以综合试验台数据为目标域数据，构建数据集；采用欠采样与合成少数类过采样技术对全寿命周期数据集进行扩充与平衡，得到了卷积神经网络训练所需的有效样本数量；在时域和频域上提取描述轴承退化过程的特征，利用卷积神经网络，遵循轴承性能退化规律的浴缸曲线，对基本特征进行融合, 构造了健康评估指标。分析结果表明: 在电机轴承轴电流损伤的健康评估中，所提出的基于迁移学习和卷积神经网络的健康评估方法的准确率为98.17%，遵循直线型、二次函数型和抛物线型退化规律构建健康指标的方法的准确率分别为86.61%、89.56%、91.30%，因此，所提评估方法准确率最大，具有更佳的评估效果，并且实现专家知识与神经网络学习知识的结合，降低了故障特征维度，解决了健康指标构建困难的问题，通过跨设备迁移学习实现了牵引电机轴承的健康评估。Abstract: To address the difficulties in acquiring labeled life-cycle vibration data and constructing health indicators that can reflect the bearing performance degradation trends in the health assessment of traction motor bearing, a health assessment method based on a transfer learning and convolutional neural network model was proposed for implementing the health assessment of traction motor bearing. The labeled bearing life-cycle data set was used as the source domain data by using the transfer learning, and the comprehensive test bench data were used as the target domain data to construct the data set. Undersampling and synthetic minority over-sampling techniques were used to expand and balance the life-cycle data set, and the abundant sample for convolutional neural networks training was obtained. The features describing the bearing degradation process were extracted in time domain and frequency domain. By using convolutional neural network and following the bathtub curve of bearing performance degradation, the health indicator was constructed by fusing the basic characteristics. Analysis results show that in the health assessment of traction motor bearing shaft current damage, the accuracy of the proposed health assessment method based on transfer learning and convolutional neural network is 98.17%. The accuracies of the methods constructing the health indicator according to linear, quadratic function and parabolic degradation law are 86.61%, 89.56% and 91.30%, respectively. Therefore, the proposed health assessment method has higher accuracy and better evaluation effect in the application. Moreover, the combination of expert knowledge and neural network learning knowledge reduces the fault characteristic dimension, solves the difficulty of health indicator construction, and realizes the health assessment of traction motor bearings through cross-device transfer learning.
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图 2 基于迁移CNN的牵引电机轴承健康评估流程
Figure 2. Health assessment flow of traction motor bearing based on transfer CNN
表 1 XJTU-SY全寿命周期轴承数据集(部分)
Table 1. XJTU-SY full life cycle bearing data set (partial)
工况1 工况2 工况3 XJ11、XJ12、XJ13、XJ15 XJ21、XJ22、XJ23、XJ24、XJ25 XJ31、XJ33 
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表 2 CNN架构
Table 2. CNN architecture
层 类型 参数 训练参数 1 输入层 12×32×1 填充=‘same’，初始学习率为0.001，最大训练次数为80，小批量数为64 2 卷积层1 C=11, N=32, S=1 3 批量归一化层1 N=32 4 线性整流函数激活层1 N=32 5 平均池化层1 C=2, N=32, S=2 6 卷积层2 C=11, N=32, S=2 7 批量归一化层2 N=32 8 线性整流函数激活层2 N=32 9 平均池化层2 C=2, N=32, S=2 10 卷积层3 C=11, N=32, S=2 11 批量归一化层3 N=32 12 线性整流函数激活层3 N=32 13 平均池化层3 C=2, N=32, S=2 14 全连接层 1 15 回归层
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表 3 健康评估数据集
Table 3. Health assessment data sets
源域数据 工况1 XJ111、XJ121、XJ131、XJ151、XJ152、XJ153 工况2 XJ211、XJ212、XJ213、XJ221、XJ222、XJ231、XJ241、XJ251 工况3 XJ311、XJ312、XJ331、XJ332、XJ333 目标域数据 训练集 QY111、QY112、QY113、QY211、QY212、QY213、QY311、QY312、QY313 测试集 QY12、QY22、QY32
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表 4 评估结果
Table 4. Evaluation results
线型 准确率/% RMSE MAE 反双曲正切 98.17 0.039 8 0.031 4 直线型 86.61 0.070 3 0.053 8 二次函数型 89.56 0.063 8 0.049 8 抛物线型 91.30 0.056 8 0.044 0
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