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摘要:
对航空发动机运行数据进行数据挖掘的方法，是发动机故障诊断研究领域的重要研究内容。由于各种算法自身的局限性，通过某种单一算法很难大幅度提升故障分类的准确性。运用组合分类的AdaBoost算法，综合多个分类模型进行诊断，是提升故障识别精度的一种较好的方法。通过AdaBoost算法及其改进算法的结合，建立一种多分类的AdaBoost算法，以支持向量机（SVM）为基础分类器，进行综合诊断模型的建立。通过单位向量法、比值系数法和相关系数法将指印图中统计的故障标识数据进行处理，得到不受故障程度影响的训练数据，再进行建模。实验表明，AdaBoost相关结合算法能够显著提升分类器性能。根据实际故障案例，验证了所建立的诊断模型能够较好地用于发动机的故障诊断。
Abstract:The data mining of aeroengine operational data is an important research for engine fault diagnosis. Due to the limitations of various algorithms, the accuracy of fault classification is difficult to be greatly enhanced with a single algorithm. Using a combination of classifications and diagnosis of multiple classification models, AdaBoost algorithm is a good method to improve the fault recognition accuracy. This paper combined the AdaBoost algorithm and its improved algorithm, and established a multi-classification AdaBoost algorithm. Support vector machine (SVM) was taken as the basic classifier, and a comprehensive diagnostic model was established. Fault identification data in statistics of fingerprint maps were processed with unit vector, ratio coefficient and correlation coefficient, and the training data for fault diagnosis with few effects of fault degrees were obtained. Then the model was constructed. The experimental results illustrate that the AdaBoost based combination algorithm can significantly improve the performance of classifier. With the actual fault cases, it is verified that the established diagnostic model can be well applied to engine fault diagnosis.
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Key words:
- AdaBoost /
- support vector machine (SVM) /
- unit vector /
- ratio coefficient /
- correlation coefficient /
- fault diagnosis
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图 2 发动机故障诊断的多分类AdaBoost算法流程图
Figure 2. Flowchart of multi-classification AdaBoost algorithm for engine fault diagnosis
图 6 不同模型诊断错误率随弱分类器个数的变化
Figure 6. Variation of diagnosis error rate of different models with number of weak classifier
表 1 PW4000指印图故障偏差数据
Table 1. Fault deviation data of PW4000 fingerprint map
故障序号 故障类别 ΔEGT/℃ ΔFF/% ΔN2/% ΔN1/% 1 +5℃ TAT -17.0 -1.4 -1.0 -1.0 2 -5℃ TAT 17.0 1.4 1.0 1.0 3 +0.02MACH 2.0 -2.2 -0.1 -0.1 4 -0.02MACH -2.0 2.2 0.1 0.1 5 +500 ALT 0 2.4 0 0 6 -500 ALT 0 -2.4 0 0 7 -2% HPC 12.0 1.6 0 0 24 -2% LPT -2.0 -2.1 0.7 -1.7 
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表 2 单位向量法故障标识
Table 2. Fault identification of unit vector method
故障序号 ΔEGT/℃ ΔFF/% ΔN2/% ΔN1/% ΔEGT/ΔFF 1 -0.993 -0.082 -0.058 -0.058 12 2 0.993 0.082 0.058 0.058 12 3 0.672 -0.739 -0.034 -0.034 -1 4 -0.672 0.739 0.034 0.034 -1 5 0 1 0 0 0 6 0 -1 0 0 0 7 0.991 0.132 0 0 8 24 -0.583 -0.612 0.204 -0.495 1
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表 3 交叉验证法中最优参数下的正确率和经AdaBoost算法提升后的正确率
Table 3. Comparison between accuracy of cross-validation method with the best parameters and that improved by AdaBoost algorithm
% 故障诊断模型 弱分类器最高正确率 应用AdaBoost算法后正确率 比值系数法 77.00 97.3 相关系数法 70.10 87.50 单位向量法 64.38 86.52 单位向量法
(加入ΔEGT/ΔFF)83.04 87.45
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表 4 相关系数法中数值相似的故障标识
Table 4. Numerical approximation fault identification of correlation coefficient method
故障序号 1 2 3 4 5 6 7 8 9 … 24 1 1 -1 -0.803 0.803 0.311 -0.311 -0.994 -0.995 -0.993 … 0.377 7 -0.994 0.994 0.734 -0.734 -0.208 0.208 1 0.999 0.999 … -0.435 8 -0.995 0.995 0.740 -0.740 -0.217 0.217 1 1 1 … -0.422 9 -0.994 0.994 0.734 -0.734 -0.208 0.208 0.999 1 1 … -0.405 24 0.377 -0.377 0.031 -0.031 -0.414 0.414 -0.435 -0.422 -0.405 … 1
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表 5 案例诊断结果
Table 5. Cases' diagnosis results
故障诊断模型 故障序号 案例1 案例2 案例3 比值系数法(1) 7 7 20 比值系数法(2) 7 7 1 相关系数法(2) 8 7 1 单位向量法(1) 7 7 1 单位向量法(2) 7 12 1 单位向量法(加入ΔEGT/ΔFF)(2) 7 7 2
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