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摘要:
针对多传感器长序列数据下航空发动机剩余使用寿命预测方法存在预测准确度不足的问题，提出一种基于堆叠膨胀卷积神经网络(SDCNN)的航空发动机剩余使用寿命预测方法。将多传感器长序列数据归一化处理，降低因量纲和取值范围不同引起的误差；构建预测目标函数表征航空发动机的真实退化情况；搭建基于SDCNN的预测模型，扩大模型感受野，提取数据中的长期、深层和全局时序特征用于回归分析，得到航空发动机的剩余使用寿命预测结果；采用Hyperband优化算法和StratifiedKFold交叉验证方法优化模型，提升模型预测准确度和不同条件下的适应性，并采用商用模块化航空推进系统仿真(C-MAPSS)数据集验证所提方法的有效性。在C-MAPSS中FD003数据集上的实验结果表明：所提方法可有效提高基于长序列的航空发动机剩余使用寿命预测准确度，模型预测准确度得分指标明显降低32.62%。
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关键词:
- 堆叠膨胀卷积 /
- 剩余使用寿命预测 /
- Hyperband超参数优化算法 /
- 航空发动机 /
- 长序列信号
Abstract:A method for forecasting the remaining useful life of an aero-engine based on a stack-dilated convolution neural network (SDCNN) was presented in order to address the inadequate prediction accuracy of the engine’s useful life with long-sequence data from many sensors.The multi-sensor long sequence data was normalized to eliminate errors caused by different dimensions and value ranges. A prediction objective function was constructed to represent the real degradation of the aero-engine. A degradation prediction model was built, based on SDCNN, and long-term, deep, and global time series features were extracted by expanding the receptive field of the model for regression analysis, and then the remaining useful life prediction result of aero-engine was obtained.The model’s hyperparameters were optimized using the Hyperband optimization algorithm and the StratifiedKFold cross-validation method to increase prediction accuracy and adaptability under various conditions. The commercial modular aero-propulsion system simulation (C-MAPSS) dataset was used to confirm the efficacy of the suggested method. The experimental results based on the FD003 dataset in C-MAPSS show that the proposed method can effectively improve the prediction accuracy of aero-engine remaining life based on long-sequence signals, and the score index to evaluate the prediction accuracy of the model is significantly reduced by 32.62%.
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图 2 3层堆叠膨胀卷积示意图
Figure 2. Schematic diagram of three-layer stacked dilated convolution
图 7 FD003数据集中1号引擎的4种传感器数据
Figure 7. 4 types of sensor data from engine No. 1 in FD003 dataset
图 9 FD003数据集中部分变量的特征信息
Figure 9. Characterization information of some variables in FD003 dataset
图 11 本文方法不同堆叠块的预测效果
Figure 11. Prediction effect of different stacked blocks in the proposed method
图 12 本文方法不同堆叠块对测试集中34号、96号引擎预测效果
Figure 12. Prediction effect of different stacked blocks in the proposed method on No. 34 and No. 96 engines of test set
图 14 本文方法对测试集中34号、96号引擎预测效果
Figure 14. Prediction effect of the proposed method on No. 34 and No. 96 engines of test set
图 15 本文方法与常用预测方法的预测效果
Figure 15. Prediction effect of the proposed method and commonly used prediction methods
图 16 本文方法与常用预测方法对测试集中34号、96号引擎预测效果
Figure 16. Prediction effect of the proposed method and commonly used prediction methods on No. 34 and No. 96 engines of test set
表 1 本文方法不同堆叠块的预测结果
Table 1. Prediction results for different stacked blocks in the proposed method
堆叠块层数 得分 均方根误差 9 91.10 10.55 10 80.05 10.20 11 73.41 10.42 12 37.41 9.01 13 107.81 11.75 
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表 2 本文方法与常用预测方法的实验结果
Table 2. Experimental results of the proposed method and commonly used prediction methods
预测方法 得分 均方根误差 LSTM 1796.28 35.12 CNN 1126.72 23.80 BiLSTM 214.07 17.38 CNN+LSTM 80.14 9.75 本文方法 37.41 9.01
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