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摘要: 针对轴承全寿命周期数据获取困难、训练样本少的问题, 提出一种基于关系网络的轴承剩余使用寿命(Remaining useful life, RUL)预测方法. 关系网络是一种基于度量的元学习方法, 在少量训练样本下, 具有快速学习新任务的优点. 设计了一种基于关系网络的轴承健康评估模型, 利用关系网络的嵌入模块提取轴承状态特征, 利用关系模块度量轴承状态特征之间的相似性, 基于相似性构建轴承健康指标(Health indicator, HI); 对健康指标进行Savitzky-Golay滤波平滑处理, 降低振荡对预测结果的影响; 最后利用线性函数对健康指标进行拟合, 得到轴承RUL预测值. 为验证所提方法的有效性, 在PHM2012轴承实测数据集上进行实验. 结果表明, 所得健康指标能够反映轴承的退化趋势, 所得RUL预测结果与空间卷积长短期记忆神经网络 (Convolutional long short-term memory neural network, ConvLSTM)、Transformer、循环神经网络(Recurrent neural network, RNN)、卷积神经网络(Convolutional neural network, CNN) + 长短期记忆网络 (long short-term memory network, LSTM )、编码器−解码器(Encoder-decoder) + 注意力机制 (Attention mechanism)方法相比, 误差百分比分别减少了1.67%, 3.40%, 9.02%, 13.71%, 30.48%. 该方法在少量训练样本的基础上可以取得较好的预测结果, 具有一定的应用价值.Abstract: To solve the problems of difficult acquisition of bearing life cycle data and few training samples, this study proposes a prediction method of bearing remaining useful life (RUL) based on a relation network. A relation network is a meta-learning method based on metric learning. It has the advantage of learning new tasks quickly with a few training samples. A bearing health assessment model based on relation network is designed. The embedded module of the relation network is used to extract the bearing state features, the relational module is used to measure the similarity between the bearing state features, and the health indicator (HI) of bearing is constructed based on the similarity. The health indicators were smoothed by Savitzky-Golay filter to reduce the impact of oscillation on the prediction results. Finally, the linear function is used to fit the health index, and the predicted value of bearing RUL is obtained. To verify the effectiveness of the proposed method, experiments are conducted on the measured bearing dataset of PHM2012. The results show that the obtained health indicators can reflect the degradation trend of the bearing. Compared with ConvLSTM (convolutional long short-term memory neural network), Transformer, RNN (recurrent neural network), CNN + LSTM (convolutional neural network + long short-term memory network), encoder-decoder + attention mechanism methods, the error percentages of the obtained RUL prediction results are reduced by 1.67%, 3.40%, 9.02%, 13.71%, and 30.48%, respectively. This method can obtain better prediction results based on a few training samples and has a certain application value.
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Key words:
- Bearing /
- remaining useful life (RUL) /
- health indicator (HI) /
- relation network /
- meta-learning
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表 1 PHM2012轴承数据集
Table 1 PHM2012 bearing dataset
工况 工况1 工况2 工况3 训练集 轴承1_1 轴承2_1 轴承3_1 轴承1_2 轴承2_2 轴承3_2 轴承1_5 轴承2_4 轴承1_6 轴承2_5 轴承1_7 轴承2_7 测试集 轴承1_3 轴承2_3 轴承3_3 轴承1_4 轴承2_6 
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表 2 不同模型参数量对比
Table 2 Comparison of different model parameters
方法 参数量 (k) 本文方法 78.61 ConvLSTM 220.50 Transformer 6461.44 CNN+LSTM 1136.64
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表 3 轴承RUL预测结果
Table 3 Bearing RUL prediction results
轴承 当前时刻 (10 s) 真实寿命 (10 s) 预测寿命 (10 s) 本文方法 (%) 文献 [13] (%) 文献 [28] (%) 文献 [11] (%) 文献 [29] (%) 文献 [12] (%) 轴承1_3 1801 573 937 −63.53 33.68 74.17 74.17 54.73 7.62 轴承1_4 1138 290 338 −16.55 47.24 −0.69 −0.69 38.69 −157.71 轴承2_3 1201 753 1005 −33.46 −32.80 61.36 61.36 75.53 81.24 轴承2_6 571 129 131 −1.55 8.52 0.78 0.78 17.87 24.92 轴承3_3 351 82 77 6.09 7.32 1.22 1.22 2.93 2.09 平均误差 — — — 24.24 25.91 27.64 33.26 37.95 54.72
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