Remaining Useful Life Prediction of Rolling Bearings Based on Attention Mechanism and CNN-BiLSTM
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摘要: 针对传统深度学习方法对滚动轴承剩余使用寿命（RUL）预测准确性不高的问题，提出一种基于注意力机制的卷积神经网络和双向长短期记忆网络的混合RUL模型（CNN-BiLSTM-AM），并运用该混合模型对滚动轴承的RUL进行预测。首先通过轴承原始振动信号的峭度特征确定轴承首次预测时间（FPT）；其次对FPT后的原始振动信号进行降噪、归一化处理并通过CNN-BiLSTM-AM混合模型对2种不同工况下的滚动轴承的RUL进行预测；最后，将CNN-BiLSTM-AM混合模型与几种传统模型进行对比。结果表明，CNN-BiLSTM-AM混合模型对滚动轴承的RUL更为有效，并具有泛化性能。
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关键词:
- 滚动轴承 /
- 剩余使用寿命(RUL) /
- 注意力机制 /
- 卷积神经网络 /
- 双向长短期记忆网络
Abstract: Aiming at the problem that traditional deep learning methods do not have high accuracy in predicting the remaining useful life (RUL) of rolling bearings, a hybrid RUL model (CNN-BiLSTM-AM) based on attention mechanism convolutional neural network and bidirectional long short-term memory network is proposed, and the model is used to predict the RUL of rolling bearings. Firstly, the bearing first prediction time (FPT) is determined by the steepness characteristics of the bearing’s original vibration signal. Secondly, the noise reduction and normalization of the original vibration signal after FPT are carried out, and the RUL of rolling bearings under two different conditions is predicted by the hybrid model CNN-BiLSTM-AM. Finally, the hybrid model CNN-BiLSTM-AM is compared with several traditional models. The results show that the hybrid model CNN-BiLSTM-AM is more effective for the RUL of rolling bearings and has generalization performance. -
图 2 LSTM结构
Ct-1—上一时间步的细胞状态；ht-1—上一时间步的隐藏状态；ft—遗忘门；it—输入门；$ {\sigma _{\text{s}}} $—sigmoid激活函数；Ct—当前时间步的细胞状态；$ \widetilde {{C_t}} $—候选记忆细胞；Ot—输出门；ht—当前时间步的隐藏状态；yt—当前时间步的输出
Figure 2. Structure of LSTM
表 1 本文所选取的轴承数据
Table 1. Bearing Data Selected in This Paper
数据子集 工况 轴承
代号轴承寿
命/minFPT/
min样本数 A 工况1（12 kN、
2100 r/min）B1_1 123 75 768 B1_2 161 43 1888 B B1_3 158 59 1584 C 工况2（11 kN、
2250 r/min）B2_2 161 47 1920 
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表 2 轴承1_3的3种指标对比
Table 2. Comparison of Three Metrics for Bearing1_3
模型名称 MSE MAE R2 CNN 0.0277 0.1277 0.7268 CNN-RNN 0.0299 0.1477 0.6416 CNN-LSTM 0.0370 0.1606 0.5560 CNN-GRU 0.0218 0.1218 0.7387 CNN-BiLSTM 0.0209 0.1214 0.7491 CNN-BiGRU 0.0277 0.1420 0.6674 CNN-BiLSTM-AM 0.0169 0.1066 0.7960
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表 3 轴承2_2的3种指标对比
Table 3. Comparison of Three Metrics for Bearing 2_2
模型名称 MSE MAE ${R^2}$ CNN 0.0648 0.2148 0.2226 CNN-RNN 0.0305 0.1465 0.6333 CNN-LSTM 0.0334 0.1565 0.5989 CNN-GRU 0.0377 0.1631 0.5476 CNN-BiLSTM 0.0309 0.1508 0.6286 CNN-BiGRU 0.0311 0.1531 0.6031 CNN-BiLSTM-AM 0.0243 0.1317 0.7075
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