基于动态软聚类的航空电子部件LMKELM诊断模型

doi:10.12305/j.issn.1001-506X.2021.03.06

Abstract

Abstract:

To improve the module-level fault diagnosis rate for avionics in a small sample size, based on the adaptive feature of dynamic fuzzy clustering and the local feature of local multiple kernel learning (LMKL), a local multiple kernel extreme learning machine (LMKELM) model for avionics is proposed. The model confirms the number of clusters adaptively by introducing the concept of local density. Local density is combined with the fuzzy C-means algorithm to realize dynamic fuzzy clustering, which well reflects the diversity between clusters and reduces calculation. A gating model is constructed to solve the non-convex quadratic problem of local weight. Fault diagnosis of test samples is realized by fusing information of local weight and membership. The proposed model is applied into a certain type of rotary transformer excitation generating circuit. Compared with the four fashionable multiple kernel learning methods, the proposed model performs better in terms of false alarm and missing alarm rate. The experimental result shows that the applied M₁ and M₂ gating models enhance the average accuracy by 2.78% and 4.37% respectively.

Key words: fault diagnosis, local density, fuzzy C-means algorithm, local multiple kernel learning, gating model

CLC Number:

V243.1

Jinling DAI, Aiqiang XU. Local multiple kernel extreme learning machine fault diagnosis model with dynamic fuzzy clustering for avionics[J]. Systems Engineering and Electronics, 2021, 43(3): 637-646.

Figures/Tables 14

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References 31

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方法	分类精度	F1	G-mean
SimpleMKL^[9]	0.856 9±0.012 1	0.862 1±0.012 4	0.858 8±0.011 9
GMKL-SVM^[17]	0.865 5±0.007 3	0.870 1±0.011 8	0.871 1±0.004 8
l₁-FCLMKELM^[29]	0.869 9±0.009 7	0.881 2±0.011 1	0.876 7±0.010 1
M₂-LCMKELM^[28]	0.876 0±0.119 0	0.884 8±0.005 0	0.879 4±0.010 3
M₁-DFCMKELM	0.880 4±0.016 5	0.884 5±0.012 1	0.883 0±0.014 3
M₂-DFCMKELM	0.893 8±0.015 9	0.891 8±0.015 8	0.8950±0.015 3

样本序号	聚类中心
样本序号	1	2	3	4
1	0.456 384	0.751 081	0.706 339	0.150 229
2	0.230 475	0.106 382	0.190 408	0.097 19
3	0.313 141	0.142 537	0.103 253	0.752 581
4	0.123 149	0.193 747	0.122 144	0.243 813
				
63	0.837 557	0.632 522	0.812 26	0.274 315

算法	漏警率	虚警率	训练准确率	故障准确率
SimpleMKL	6.349 2	0	100	88.888 9
GMKL-SVM	0	3.174 6	100	92.063 5
l₁-FCLMKELM	1.587 3	0	100	95.238 1
M₂-LCMKELM	0	0	100	93.650 8
M₁-DFCMKELM	0	0	100	95.238 1
M₂-DFCMKELM	0	0	100	96.825 4

算法	F1值	G-mean
SimpleMKL	0.901 4	0.892 3
GMKL-SVM	0.924 6	0.915 8
l₁-FCLMKELM	0.953 3	0.950 1
M₂-LCMKELM	0.942 1	0.934 7
M₁-DFCMKELM	0.957 5	0.950 3
M₂-DFCMKELM	0.970 6	0.964 8

算法	训练时间	测试时间
SimpleMKL	0.665 2±0.020 1	0.169 5±0.003 5
GMKL-SVM	0.756 4±0.018 6	0.164 8±0.002 4
l₁-FCLMKELM	1.943 2±0.047 3	0.138 3±0.011 4
M₂-LCMKELM	1.831 9±0.083 9	0.138 4±0.005 8
M₁-DFCMKELM	1.864 7±0.164 6	0.127 3±0.012 9
M₂-DFCMKELM	1.756 1±0.223 1	0.138 2±0.011 8

Local multiple kernel extreme learning machine fault diagnosis model with dynamic fuzzy clustering for avionics

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