Health management method of helicopter turbine engine based on Caps-BiGRU-Attention
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摘要:
针对直升机涡轮发动机故障难以识别、健康状况难以量化的问题，提出了基于注意力机制的胶囊网络-双向门控循环单元模型（Caps-BiGRU-Attention）来进行涡轮发动机的故障模式识别以及扭矩裕度预测。该模型由3个主要部分构成：胶囊层用于捕捉输入数据的内在关系，双向门控循环单元（bidirectional gated recurrent unit，BiGRU）层用于提取时间序列特征并输出结果，压缩激活注意力机制（squeeze-and-excitation attention mechanism，SE）对特征加权以突出重要信息。通过直升机涡轮发动机数据集进行实验验证，模型在故障诊断中准确率超过99.7%，并将扭矩裕度预测中的平均绝对误差降低至0.027；其次对诊断和预测过程进行特征分析，寻找有利于发动机健康状态的特征取值范围。最后针对扭矩裕度的分布进行了概率分布拟合，确定在发动机严重失效、轻微失效和健康状态的最佳分布为贝塔分布，为直升机涡轮发动机的健康管理提供了重要参考。
Abstract:In response to the issues of difficulties in identifying faults and quantifying the health status of helicopter turbo engines, a Caps-BiGRU-Attention model based on the attention mechanism for fault mode recognition and torque margin prediction of turbo engines was proposed. The model consisted of three main components: capsule layer used to capture the intrinsic relationships of the input data, bidirectional gated recurrent unit (BiGRU) layer used to extract time series features and outputs results, and the squeeze-and-excitation attention mechanism (SE) used to weight the features to highlight important information. Experimental validation on a helicopter turbo engine dataset demonstrated that the model achieved an accuracy exceeding 99.7% in fault diagnosis and reduced the mean absolute error in torque margin prediction to 0.027. Additionally, feature analysis was conducted during the diagnosis and prediction processes to identify favorable ranges of feature values for the engine’s health status. Finally, probability distribution fitting was performed on the distribution of torque margin, determining that the optimal distributions for severe failure, minor failure, and healthy states of the engine were beta distributions.
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图 1 Caps-BiGRU-Attention模型结构示意图
Figure 1. Schematic diagram of Caps-BiGRU-Attention model structure
图 4 Caps-BiGRU-Attention模型错分类样本置信区间
Figure 4. Confidence interval of misclassified samples for the Caps-BiGRU-Attention model
图 5 扭矩裕度预测模型训练迭代图
Figure 5. Training iteration variation diagram of the torque margin prediction model
图 6 模型测试集部分预测值与真实值误差区间对比
Figure 6. Comparison of error intervals between predicted values and true values in the algorithm test set
图 8 扭矩裕度预测模型的SHAP特征摘要图
Figure 8. SHAP feature summary plot of torque margin prediction model
图 9 扭矩裕度预测模型SHAP值随平均气体温度范围变化
Figure 9. Variation of SHAP values of torque margin prediction model with average gas temperature range
图 11 扭矩裕度预测模型净功率特征散点图
Figure 11. Scatter plot of net power in the torque margin prediction model
图 12 扭矩裕度预测模型外部空气温度特征散点图
Figure 12. Scatter plot of air temperature in the torque margin prediction model
图 13 扭矩裕度预测模型指示空速特征散点图
Figure 13. Scatter plot of indicated airspeed in the torque margin prediction model
图 14 验证集目标扭矩和扭矩裕度箱图
Figure 14. Box plot of target torque and torque margin for the validation set
图 15 p值及AIC值随混合高斯密度函数组分数变化
Figure 15. Variation of p-values and AIC values with the number of components in the mixed Gaussian density function
图 16 混合高斯密度函数拟合QQ图
Figure 16. Quantile-quantile plot of mixed Gaussian density function fitting
图 18 验证集扭矩裕度分段后概率密度函数拟合图
Figure 18. Fitted probability density function plot of segmented torque margin for the validation set
图 19 测试集扭矩裕度分段后概率密度函数拟合图
Figure 19. Fitted probability density function plot of segmented torque margin for the test set
表 1 故障数据集描述
Table 1. Description of fault dataset
数据代码 测量值 数据类别 标准单位 1 外部空气温度 特征 ℃ 2 平均气体温度 特征 ℃ 3 可用功率 特征 kW 4 指示空速 特征 km/h 5 净功率 特征 kW 6 压缩机转速 特征 r/min 7 测量扭矩 特征 N·m 8 故障模式 目标值 9 扭矩裕度 目标值 % 
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表 2 模型参数设置
Table 2. Model parameter settings
参数名称 设定值 参数名称 设定值 通道特征维度 16 批处理数量 512 动态路由次数 7 迭代次数 200 胶囊数量 6 测试集占比 0.15 胶囊维度 8 验证集占比 0.15 BiGRU层隐藏层数 2 优化器 Adam BiGRU层隐含层
单元数1024 数据输入格式 三维数组 全连接层维度 2/512 损失函数 交叉熵损失
函数全连接层输出
激活函数Softmax Dropout 0.4
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表 3 多模型性能指标对比
Table 3. Comparison of performance metrics for multiple models
领域 模型 准确率 精确率 召回率 F1分数 MCC 故障诊断 Caps-BiGRU-Attention 0.997 0.9968 0.9969 0.9969 0.9938 CNN-BiGRU-Attention 0.9681 0.9682 0.9653 0.9667 0.9336 CNN-BiLSTM 0.9543 0.9550 0.9498 0.9522 0.9048 CNN-BiGRU 0.9528 0.9540 0.9477 0.9506 0.9017 Random Forest 0.9367 0.9312 0.9417 0.9351 0.8729 领域 模型 方均误差 方均根误差 平均绝对误差 平均绝对百分比误差/% 决定系数/% 扭矩裕度预测 Caps-BiGRU-Attention 0.0014 0.0376 0.0271 3.9572 99.9992 CNN-BiGRU-Attention 0.0096 0.0982 0.0685 6.8093 99.9952 CNN-BiGRU 0.0104 0.1024 0.0688 20.6641 99.9947 CNN-BiLSTM 0.0404 0.2010 0.1488 23.6817 99.9800 Random Forest 0.0704 0.2653 0.2457 29.89 99.8584
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表 4 扭矩裕度分段后概率密度函数拟合参数
Table 4. Fitting parameters for the probability density function of segmented torque margin
数据集 目标裕度范围 p值 分布 箱数 AIC值 分布函数参数值 验证集 （−∞，−8] 0.999 正态分布 20 1143.93 0.945 伽马分布 15 1164.29 0.916 贝塔分布 20 1130.30 （ 5492493.3 ，40.6，−618299.3 ，618294.0 （−8，−3] 0.175 贝塔分布 19 18439.20 （3.8， 2.6， −8.5， 5.7） 0.127 最小威布尔分布 31 18571.63 0.117 正态分布 36 18697.90 （−3，16] 0 最小威布尔分布 32 67470.56 0 贝塔分布 10 66762.50 0 t分布 43 69269.38 （16，+∞] 0.988 贝塔分布 47 6750.68 （1.0， 11.1， 16.0， 24.0） 0.876 最小威布尔分布 16 6754.50 0.809 伽马分布 48 6763.17 测试集 （−∞，−8] 0.793 Logistic分布 31 1451.71 （−9.2， 0.25） 0.62 拉普拉斯分布 30 1478.75 0.17 t分布 29 1459.30 （−8，−5] 0.473 最小威布尔分布 19 4738.22 0.13 贝塔分布 42 4657.68 （4.0， 1.8， −8.7， 3.7） 0.009 t分布 15 4996.706 （−5，20] 0 Logistic分布 16 109707.694 0 正态分布 39 109342.603 0 t分布 37 109344.603 （20，+∞] 0.992 贝塔分布 46 1089.614 （1.2， 44.8， 20.0， 58.8） 0.962 指数分布 22 1092.213 0.962 伽马分布 47 1090.062
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