| Citation: | CAO Huiling, GAO Sheng, XUE Penget al. Aeroengine fault diagnosis based on multi-classification AdaBoost[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1818-1825. doi: 10.13700/j.bh.1001-5965.2017.0774(in Chinese)
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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.
| [1] |
WU X, KUMAR V.The top ten algorithms in data mining[M].Boca Raton:CRC Press, 2009:127-149.
|
| [2] |
曹莹, 苗启广, 刘家辰, 等.AdaBoost算法研究进展与展望[J].自动化学报, 2013, 39(6):745-758.
CAO Y, MIAO Q G, LIU J C, et al.Advance and prospects of AdaBoost algorithm[J].Acta Automatica Sinica, 2013, 39(6):745-758(in Chinese).
|
| [3] |
徐启华, 杨瑞.基于AdaBoost算法的故障诊断仿真研究[J].计算机工程与设计, 2005, 26(12):3210-3212. doi: 10.3969/j.issn.1000-7024.2005.12.016
XU Q H, YANG R.Simulation research on fault diagnosis using AdaBoost algorithm[J].Computer Engineering and Design, 2005, 26(12):3210-3212(in Chinese). doi: 10.3969/j.issn.1000-7024.2005.12.016
|
| [4] |
夏利民, 戴汝为.基于Boosting模糊分类的滚动轴承故障诊断[J].模式识别与人工智能, 2003, 16(3):323-327. doi: 10.3969/j.issn.1003-6059.2003.03.011
XIA L M, DAI R W.Fault testing on rolling bearing based on Boosting fuzzy classification[J].Pattern Recognition and Artificial Intelligence, 2003, 16(3):323-327(in Chinese). doi: 10.3969/j.issn.1003-6059.2003.03.011
|
| [5] |
孙超英, 刘鲁, 刘传武.基于Boosting-SVM算法的航空发动机故障诊断[J].航空动力学报, 2010, 25(11):2584-2588.
SUN C Y, LIU L, LIU C W.Aero-engine fault diagnosis based on Boosting-SVM[J].Journal of Aerospace Power, 2010, 25(11):2584-2588(in Chinese).
|
| [6] |
胡金海, 谢寿生, 蔡开龙, 等.Diverse AdaBoost-SVM分类方法及其在航空发动机故障诊断中的应用[J].航空学报, 2007, 28(5):1085-1090. doi: 10.3321/j.issn:1000-6893.2007.05.010
HU J H, XIE S S, CAI K L, et al.Classification method of diverse AdaBoost-SVM and its application to fault diagnosis of aero-engine[J].Acta Aeronautica et Astronautica Sinica, 2007, 28(5):1085-1090(in Chinese). doi: 10.3321/j.issn:1000-6893.2007.05.010
|
| [7] |
胡金海, 骆广琦, 李应红, 等.一种基于指数损失函数的多类分类AdaBoost算法及其应用[J].航空学报, 2008, 29(4):811-816. doi: 10.3321/j.issn:1000-6893.2008.04.007
HU J H, LUO G Q, LI Y H, et al.An AdaBoost algorithm for multi-class classification based on exponential loss function and its application[J].Acta Aeronautica et Astronautica Sinica, 2008, 29(4):811-816(in Chinese). doi: 10.3321/j.issn:1000-6893.2008.04.007
|
| [8] |
KEARNS M, VALIANT L.Cryptographic limitations on learning Boolean formulae and finite automata[J].Journal of the ACM, 1994, 41(1):67-95. doi: 10.1145/174644.174647
|
| [9] |
FREUND Y, SCHAPIRE R E.A decision-theoretic generalization of on-line learning and an application to Boosting[J].Journal of Computer and System Sciences, 1997, 55(1):119-139. doi: 10.1006/jcss.1997.1504
|
| [10] |
FREUND Y, SCHAPIRE R E.Experiments with a new Boosting algorithm[C]//Proceedings of the 13th Conference on Machine Learning, 1996: 148-156.
|
| [11] |
李斌, 王紫石, 汪卫, 等.AdaBoost算法的一种改进方法[J].小型微型计算机系统, 2004, 25(5):869-871. doi: 10.3969/j.issn.1000-1220.2004.05.020
LI B, WANG Z S, WANG W, et al.Enhancing method for AdaBoost[J].Mini-Micro Systems, 2004, 25(5):869-871(in Chinese). doi: 10.3969/j.issn.1000-1220.2004.05.020
|
| [12] |
廖红文, 周德龙.AdaBoost及其改进算法综述[J].计算机系统应用, 2012, 21(5):240-244.
LIAO H W, ZHOU D L.Review of AdaBoost and its improvement[J].Computer Systems & Applications, 2012, 21(5):240-244(in Chinese).
|
| [13] |
ZHU J, ZOU H, ROSSET S, et al.Multi-class AdaBoost[J].Statistics and Its Interface, 2009, 2(3):349-360. doi: 10.4310/SII.2009.v2.n3.a8
|
| [14] |
李应红, 尉询楷.航空发动机的智能诊断、建模与预测方法[M].北京:科学出版社, 2013:82-85.
LI Y H, WEI X K.The methods of aero-engine's modeling, intelligent fault diagnosis and prognosis[M].Beijing:Science Press, 2013:82-85(in Chinese).
|
| [15] |
FRIEDMAN J, HASTIE T, TIBSHIRANI R.Additive logistic regression:A statistical view of boosting[J].Annals of Statistics, 2000, 28(2):337-407. doi: 10.1214-aos-1016218223/
|
| [16] |
张卓.基于SVM多分类的PW4000故障诊断研究[D].天津: 中国民航大学, 2015: 42-49.
ZHANG Z.Research on fault diagnosis of PW4000 based on SVM multi-classification[D].Tianjin: Civil Aviation University of China, 2015: 42-49(in Chinese).
|
| [17] |
杨新武, 马壮, 袁顺.基于弱分类器调整的多分类AdaBoost算法[J].电子与信息学报, 2016, 38(2):373-380.
YANG X W, MA Z, YUAN S.Multi-class AdaBoost algorithm based on the adjusted weak classifier[J].Journal of Electronics & Information Technology, 2016, 38(2):373-380(in Chinese).
|
| [18] |
曹惠玲, 庞思凯, 薛鹏, 等.基于双发差异的航空发动机故障诊断方法研究[J].中国民航大学学报, 2014, 32(3):41-44. doi: 10.3969/j.issn.1674-5590.2014.03.010
CAO H L, PANG S K, XUE P, et al.Research of aero-engine fault diagnosis method based on monitoring of twin differences[J].Journal of Civil Aviation University of China, 2014, 32(3):41-44(in Chinese). doi: 10.3969/j.issn.1674-5590.2014.03.010
|
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| [13] | GAO H H，CHAO Q，XU Z，et al. Piston pump fault diagnosis based on Siamese neural network with small samples[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（1）：155-164 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0213. |
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CAO Huiling, GAO Sheng, XUE Penget al. Aeroengine fault diagnosis based on multi-classification AdaBoost[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(9): 1818-1825. doi: 10.13700/j.bh.1001-5965.2017.0774(in Chinese)
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