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摘要:
剩余使用寿命(RUL)的准确预测对航空发动机的稳定性、可靠性和安全性至关重要。针对现有RUL预测方法无法充分挖掘传感器数据退化特征的问题，提出一种基于深度学习模型BSVAR的航空发动机RUL预测方法。采用双向长短期记忆(Bi-LSTM)网络与基于自注意力机制的变分自编码器(SVAE)提取传感器数据的深层次退化信息；基于变分推理对传感器数据具有的退化特征进行聚类，生成潜在空间；将Bi-LSTM、SVAE和回归器相结合建立RUL预测模型，充分提取传感器数据的退化特征，提升RUL预测精度。在航空发动机C-MAPSS数据集上进行实验验证，实验结果表明：所提方法可以在潜在空间中标记发动机的退化进程；相比于现有的RUL预测方法具有更高的预测精度。
Abstract:Accurate prediction of remaining useful life (RUL) is critical to the stability, reliability, and safety of aircraft engines. A new deep learning model called BSVAR is suggested for RUL prediction in order to address the issue that current RUL prediction techniques are unable to properly utilize the deterioration information of sensor data. The deep degradation information of sensor data is extracted using a bidirectional long-short-term memory (Bi-LSTM) networks and self-attention based variational autoencoder (SVAE). With the utilization of variational inference, the sensor data is clustered according to the implied degradation information, meanwhile, the latent space can be generated. The combination of the Bi-LSTM, the SVAE, and the regressor is used to establish a RUL prediction model to sufficiently extract the degradation features of sensor data to improve the prediction accuracy. Results from experimental validation on the aero-engine C-MAPSS dataset demonstrate that the suggested approach outperforms the current RUL prediction approaches in terms of prediction performance and can identify the engines’ degree of degradation in the latent space.
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表 1 C-MAPSS数据集
Table 1. C-MAPSS dataset
数据集 训练集发动机台数 测试集发动机台数 工作条件 故障模式 FD001 100 100 1 1 FD002 260 259 6 1 FD003 100 100 1 2 FD004 249 248 6 2 
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表 2 超参数设置
Table 2. Hyperparameter setup
参数 数值 时间窗(FD001/FD002/FD003/FD004) 25/20/30/15 潜在空间维数 2 学习率 0.001 迭代次数 10 Bi-LSTM单元个数 300 批大小 256 全连接层神经元个数 200
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表 3 与最新方法比较
Table 3. Comparison with state-of-the-art approaches
方法 RMSE Score FD001 FD002 FD003 FD004 FD001 FD002 FD003 FD004 DCNN[5] 12.61 22.36 12.64 23.31 273.7 10412 284.1 12466 Bi-LSTM[8] 25.11 26.61 4793 4971 HDNN[9] 13.01 15.24 12.22 18.156 245 1282.42 287.72 1527.42 BGRU-TSAM[10] 12.56 18.92 12.45 20.47 213.35 2264.13 232.86 3610.34 Dual-Att[12] 12.25 17.08 13.39 19.68 198 1575 290 1741 BSVAR 12.39 14.88 13 15.52 246.58 854.11 317.51 1160.17 EN[14] 13.58 19.59 19.16 22.15 228 2650 1727 2901 RVE[20] 13.42 14.92 12.51 16.37 323.82 1379.17 256.36 1845.99
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表 4 所选4台发动机的RUL值
Table 4. RUL values for the selected four engines
发动机编号 真实RUL值 预测RUL值 1# 112 118.34 11# 97 95.64 77# 34 23.55 100# 20 19.07
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