基于隶属度和LMK-ELM的航空电子部件诊断方法

doi:10.7527/S1000-6893.2019.23277

Abstract

Abstract: To improve the accuracy of module-level fault diagnosis for avionics, a new off-line diagnosis method based on soft-clustering-sensitive Localized Multi-Kernel Learning (LMKL) and Extreme Learning Machine (ELM) is proposed. By introducing fuzzy C-means clustering to partition the sample space, the over-learning is suppressed while mining the diversity within the cluster. The membership information generated by the fuzzy partition is integrated into the optimization process of LMKL-ELM. A three-step optimization strategy based on the initial-dual hybrid optimization problem is used to overcome the quadratic non-convexity of the local kernel weights. The corresponding updating methods for these weights are given under l₁-norm constraint and l₂-norm constraint. The proposed method is applied to the front-end receiver. Compared with four popular multi-kernel diagnostic algorithms, the results show that the proposed method can effectively avoid missing alarm and suppress false alarm. The diagnostic accuracy is 4.09% higher in l₁-norm and 5.13% higher in l₂-norm than the average of other methods.

Key words: extreme learning machine, localized multiple kernel learning, fuzzy C-means clustering, fault diagnosis, avionic

CLC Number:

V231.1

ZHU Min, XU Aiqiang, LI Ruifeng, DAI Jinling. Diagnosis method for avionics based on membership and LMK-ELM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(12): 323277-323277.

References 32

[1]	孙伟超, 李文海, 李文峰. 融合粗糙集与D-S证据理论的航空装备故障诊断[J]. 北京航空航天大学学报, 2015, 41(10):1902-1909. SUN W C, LI W H, LI W F. Avionic devices fault diagnosis based on fusion method of rough set and D-S theory[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10):1902-1909(in Chinese).
[2]	KNÜPPEL T, BLANKE M, ØSTERGAARD J. Fault diagnosis for electrical distribution systems using structural analysis[J]. International Journal of Robust and Nonlinear Control, 2014, 24:1446-1465.
[3]	JAMIL T, MOHAMMED I. Simulation of VICTOR algorithm for fault diagnosis of digital circuits[J]. International Journal of Computer Theory and Engineering, 2015, 7(2):103-107.
[4]	DAI X W, GAO Z W. From model, signal to knowledge:A data-driven perspective of fault detection and diagnosis[J]. IEEE Transactions on Industrial Informatics, 2013, 9(4):2226-2238.
[5]	GAO Z W, CECATI C, DING S X. A survey of fault diagnosis and fault tolerant techniques-Part I:Fault diagnosis with model-based and signal-based approaches[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6):3757-3767.
[6]	LIU Z B, JIA Z, VONG C M, et al. Capturing high-discriminative fault features for electronics-rich analog system via deep learning[J]. IEEE Transactions on Industrial Informatics, 2017, 13(3):1213-1226.
[7]	赵光权, 葛强强, 刘小勇, 等. 基于DBN的故障特征提取及诊断方法研究[J]. 仪器仪表学报, 2016, 37(9):1946-1953. ZHAO G Q, GE Q Q, LIU X Y, et al. Fault feature extraction and diagnosis based on deep belief network[J]. Chinese Journal of Scientific Instrument, 2016, 37(9):1946-1953(in Chinese).
[8]	李可, 王全鑫, 宋世民, 等. 基于改进人工神经网络的航天器电信号分类方法[J]. 北京航空航天大学学报, 2016, 42(3):596-601. LI K, WANG Q X, SONG S M, et al. Spacecraft electrical signal classification method based onimproved artificial neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(3):596-601(in Chinese).
[9]	ZHANG M J, CHAI K, HUANG J, et al. Combined improved EEMD with SVM in the bearing low dimensional small sample fault diagnosis[J]. Applied Mechanics & Materials, 2013, 427:354-357.
[10]	GUO Y, MA J, XIAO F, et al. SVM with optimized parameters and its application to electronic system fault diagnosis[C]//Prognostics and Health Management. Piscataway, NJ:IEEE Press, 2012:1-6.
[11]	张伟, 许爱强, 平殿发, 等. 基于近邻传播聚类的航空电子部件LMK诊断模型[J]. 北京航空航天大学学报, 2018, 44(8):1693-1704. ZHANG W, XU A Q, PING D F, et al. Localized multi-kernel diagnosis model for avionics based on affinity propagation clustering[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(8):1693-1704(in Chinese).
[12]	闫涛, 赵文俊, 胡秀洁, 等. 基于信息融合技术的航空电子设备故障诊断研究[J]. 电子科技大学学报, 2015, 44(3):392-396. YAN T, ZHAO W J, HU X J, et al. Fault diagnosis of avionic devices based on information fusion technology[J]. Journal of University of Electronic Science and Technology of China, 2015, 44(3):392-396(in Chinese).
[13]	吕克洪, 程先哲, 李华康, 等. 电子设备故障预测与健康管理技术发展新动态[J]. 航空学报, 2019, 40(11):323285. LYU K H, CHENG X Z, LI H K, et al. New developments of prognostic and health management technology for electronic equipment[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(11):323285(in Chinese).
[14]	刘艳芳, 吕江花, 马世龙, 等. 航电系统并行检测过程与检测设备解耦方法[J]. 航空学报, 2019, 40(8):322818. LIU Y F, LYU J H, MA S L, et al. Decoupling method for test process and test devices in parallel testing of avionics systems[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(8):322818(in Chinese).
[15]	邴其春, 龚勃文, 杨兆升, 等. 一种组合核相关向量机的短时交通流局域预测方法[J]. 哈尔滨工业大学学报, 2017, 49(3):144-149. BING Q C, GONG B W, YANG Z S, et al. A short-term traffic flow local prediction method of combined kernel function relevance vector machines[J]. Journal of Harbin Institute of Technology, 2017, 49(3):144-149(in Chinese).
[16]	李军, 李大超. 基于优化核极限学习机的风电功率时间序列预测[J]. 物理学报, 2016, 65(13):39-48. LI J,LI D C. Wind power time series prediction using optimized kernel extreme learning machine method[J]. Acta Physica Sinica, 2016, 65(13):39-48(in Chinese).
[17]	FENG J, JIAO L C, SUN T, et al. Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection[J]. IEEE Transactions on Geosciences and Remote Sensing, 2016, 54(11):6516-6530.
[18]	GÖNEN M, ALPAYDIN E. Multiple kernel learning algorithms[J]. Journal of Machine Learning Research, 2011, 12:2211-2268.
[19]	ZHANG C L, HE Y G, YUAN L F, et al. A novel approach for diagnosis of analog circuit fault by using GMKL-SVM and PSO[J]. Journal of Electronic Testing, 2016, 32:531-540.
[20]	LI Y X, REN C Q, BO J Y, et al. The application of GMKL algorithm to fault diagnosis of local area network[J]. Journal of Networks, 2014, 9(3):747-753.
[21]	RAKOTOMAMONJY A, BACH F R, CANU S, et al. SimpleMKL[J]. Journal of Machine Learning Research, 2008, 9:2491-2521.
[22]	LIU X W, WANG L, ZHANG J, et al. Sample-adaptive multiple kernel learning[C]//28th AAAI Conference on Artificial Intelligence, 2014:1975-1981.
[23]	HAN Y N, YANG K D, MA Y L, et al. Localized multiple kernel learning via sample-wise alternating optimization[J]. IEEE Transactions on Cybernetics, 2014, 44(1):137-147.
[24]	GÖNEN M, ALPAYDIN E. Localized algorithms for multiple kernel learning[J]. Pattern Recognition, 2013, 46:795-807.
[25]	HAN Y N, YANG K D, YANG Y X, et al. On the impact of regularization variation on localized multiple Kernel learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(6):2625-2630.
[26]	YANG J J, LI Y N, TIAN Y H, et al. Group-sensitive multiple kernel learning for object recognition[J]. IEEE Transactions on Image Processing, 2012, 21(5):2838-2852.
[27]	HAN Y N, YANG K D, YANG Y X, et al. Localized multiple Kernel learning with dynamical clustering and matrix regularization[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(2):486-499.
[28]	LIU X W, WANG L, HUANG G B, et al. Multiple kernel extreme learning machine[J]. Neurocomputing, 2015, 149:253-264.
[29]	BEZDEK J C, EHRLICH R, FULL W. FCM:The fuzzy c-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2-3):191-203.
[30]	SOKOLOVA M, LAPALME G. A systematic analysis of performance measures for classification tasks[J]. Information Processing and Management, 2009, 45(4):427-437.
[31]	PHOUNGPHOL P, ZHANG Y Q, ZHAO Y C. Robust multiclass classification for learning from imbalanced biomedical data[J]. Tsinghua Science and Technology, 2012, 17(6):619-628.
[32]	MAATEN L, HINTON G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(11):2579-2605.

Diagnosis method for avionics based on membership and LMK-ELM

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