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摘要: 针对实际中某种工况滚动轴承带标签振动数据获取困难, 健康指标难以构建及寿命预测误差大的问题, 提出一种基于无监督深度模型迁移的滚动轴承剩余使用寿命(Remaining useful life, RUL)预测方法. 该方法首先对滚动轴承全寿命周期振动数据提取均方根(Root mean square, RMS)特征, 并引入新的自下而上(Bottom-up, BUP)时间序列分割算法将特征序列分割为正常期、退化期和衰退期3种状态; 对振动信号经快速傅里叶(Fast Fourier transform, FFT)变换后的幅值序列进行状态信息标记, 并将其输入到新增卷积层的全卷积神经网络(Full convolutional neural network, FCN)中, 提取深层特征, 得到预训练模型; 提出将预训练模型的梯度作为一种“特征”与传统预训练模型特征一起参与目标域网络训练过程, 从而得到状态识别模型; 利用状态概率估计法结合状态识别模型建立滚动轴承寿命预测模型. 实验验证所提方法无需构建健康指标, 可实现无监督条件下不同工况滚动轴承剩余寿命预测, 并获得较好的效果.Abstract: In order to solve the problems such as difficulty in acquiring labeled vibration data of rolling bearings under certain working condition in practice, difficulty in constructing health indicators and large error in life prediction of rolling bearings, a method of remaining useful life (RUL) prediction of rolling bearings is proposed based on unsupervised deep model transfer. Firstly, the root mean square (RMS) features of the vibration data of the full life cycle of the rolling bearings are extracted, and a new bottom-up (BUP) time series segmentation algorithm is introduced to divide the feature sequence into three states: Normal period, degradation period and recession period. Mark the state information of the amplitude sequence of the vibration signal after the fast Fourier transform (FFT), and input it into the fully convolutional neural network (FCN) of the newly added convolutional layer to extract deep features, and the pre-trained model can be obtained. The gradient of the pre-trained model is proposed and used as a “feature” to participate in the target domain network training process together with the traditional pre-trained model features, and the state identification model is obtained. Using state probability estimation method combined with state identification model, life prediction model of rolling bearing can be established. Experiments verify that, without establishing health indicators, the proposed method can realize remaining useful life prediction of rolling bearings for different working conditions under unsupervised conditions, and achieve better results.
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图 5 滚动轴承剩余寿命预测流程框图
Fig. 5 Block diagram of remaining life prediction process of the rolling bearing
图 9 轴承2_7迁移之前训练及测试损失值
Fig. 9 Training and testing loss values for bearing 2_7 before transferring
图 10 轴承2_7迁移之后训练及测试损失值
Fig. 10 Training and testing loss values for bearing 2_7 after transferring
表 1 PHM 2012数据描述
Table 1 PHM 2012 data description
数据 工况 1 工况 2 工况 3 训练数据 轴承1_1 轴承2_1 轴承3_1 轴承1_2 轴承2_2 轴承3_2 测试数据 轴承1_3 轴承2_3 轴承3_3 轴承1_4 轴承2_4 轴承1_5 轴承2_5 轴承1_6 轴承2_6 轴承1_7 轴承2_7 
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表 2 三种工况描述
Table 2 Description of the three working conditions
工况 转速 (r/min) 载荷 (N) 工况 1 1800 4000 工况 2 1650 4200 工况 3 1500 5000
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表 3 卷积层数修改前后实验结果
Table 3 Experimental results before and after modification of the number of convolutional layers
层数 准确率 (%) 原始卷积层数目 3 94.23 修改后卷积层数目 4 99.46
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表 4 传递梯度特征前后实验对比结果
Table 4 Experimental comparison results before and after transferring gradient features
测试集 平均准确率 (%) 不传递梯度特征 传递梯度特征 1_3 93.95 99.86 1_4 99.29 99.91 1_5 90.92 99.91 1_6 92.87 99.56 1_7 87.90 98.40 2_3 92.84 99.92 2_4 94.60 99.67 2_5 91.35 98.46 2_6 93.70 99.57 2_7 88.26 99.46 3_3 92.04 99.01
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表 5 不同轴承RUL预测误差结果对比
Table 5 Comparison of RUL prediction error results of different bearings
不同轴承 当前时间点 实际预测点 本文预测点 本文误差$E_J $ (%) 1_3 18010 5730 4270 25.35 1_4 11380 2900 2210 23.53 1_5 23010 1610 3630 −126.87 1_6 23010 1460 1540 −5.47 1_7 15010 7570 2930 61.24 2_3 12010 7530 2950 60.77 2_4 6110 1390 1180 14.49 2_5 20010 3090 350 88.63 2_6 5710 1290 1110 13.28 2_7 1710 580 330 42.11 3_3 3510 820 680 16.05 平均误差 — — — 19.37 平均得分 — — — 0.33
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表 6 与其他方法预测误差结果对比
Table 6 Comparison of prediction error results with other methods
不同轴承 预测误差$E_J $ (%) 本文方法 方案1 方案2 文献 [25] 文献 [26] 1_3 25.35 35.98 32.54 43.28 −31.76 1_4 23.53 40.25 36.92 67.55 62.76 1_5 −126.87 −138.54 −129.35 −22.98 −136.03 1_6 −5.47 −20.18 −15.18 21.23 −32.88 1_7 61.24 79.65 75.64 17.83 −11.09 2_3 60.77 80.25 72.49 37.84 44.22 2_4 14.49 30.24 25.83 −19.42 −55.40 2_5 88.63 100.25 95.95 54.37 68.61 2_6 13.28 35.68 30.39 −13.95 −51.94 2_7 42.11 60.21 55.16 −55.17 −68.97 3_3 16.05 40.81 35.97 3.66 −21.96 平均误差 19.37 31.33 28.76 32.48 53.24
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