基于LMKL和OC-ELM的航空电子部件故障检测方法

doi:10.3969/j.issn.1001-506X.2020.06.29

Abstract

Abstract:

In consideration of the difficulty in acquiring real fault samples and the low detection accuracy of the avionics, a fault detection method based on localized multiple kernel learning (LMKL) and one-class extreme learning machine (OC-ELM) is proposed. The mathematical expression of LMK-OC-ELM is given onlyby using small sample data in normal state, and the localized kernel weights in LMK-OC-ELM is deduced under different gating models. On the basis of obtaining the localized kernel weights, the test statistic and threshold required for offline fault detection are defined to facilitate the engineering implementation. The proposed method is applied to the receiver. On the premise of controllable training time, the proposed method can improve recall, precision and specificity equitably compared with the other four common one-class classification algorithms. Taking LMK-OC-ELM-sig as the representative, the indicators of F1, area under curve (AUC), G-mean and accuracy are increased by 1.60%, 1.57%, 1.53% and 2.23% respectively compared with the localized multiple kernel anomaly detection (LMKAD) which is recently proposed.

Key words: extreme learning machine (ELM), localized multiple kernel learning (LMKL), one-class classification (OCC), fault detection

CLC Number:

V243

Min ZHU, Qi LIU, Xing LIU, Qing XU. Fault detection method for avionics based on LMKL and OC-ELM[J]. Systems Engineering and Electronics, 2020, 42(6): 1424-1432.

Figures/Tables 10

Table 1

Table 2

Experimental results of OCC on UCI datasets"

数据集	算法	F1	AUC	G-mean	准确率	参数取值
Ecoli	SVDD	0.448 3	0.807 9	0.601 8	0.920 2	σ=3.439 5
	PCA	0.342 5	0.774 1	0.421 3	0.736 5	n=3
	OC-SVM	0.459 8	0.816 2	0.520 6	0.797 7	γ=0.047 5
	OC-KELM	0.778 4	0.903 2	0.780 3	0.960 5	C=1, σ=3.223 5
	LMKAD	0.594 8	0.857 8	0.633 4	0.865 8	-
	LMK-OC-ELM-so	0.782 4	0.896 3	0.783 4	0.962 1	C=0.1
	LMK-OC-ELM-r	0.564 3	0.829 4	0.605 1	0.817 1	C=0.001
	LMK-OC-ELM-sig	0.793 1	0.894 2	0.793 4	0.964 7	C=0.1
Sonar	SVDD	0.584 2	0.623 6	0.616 0	0.559 5	σ=17.438 5
	PCA	0.532 2	0.551 4	0.569 2	0.476 0	n=3
	OC-SVM	0.570 3	0.617 9	0.593 9	0.570 4	γ=0.002 5
	OC-KELM	0.513 3	0.483 2	0.575 2	0.352 0	C=0.01, σ=8.439 1
	LMKAD	0.573 1	0.621 3	0.594 3	0.583 9	-
	LMK-OC-ELM-so	0.598 0	0.679 1	0.601 4	0.691 4	C=10
	LMK-OC-ELM-r	0.542 0	0.582 3	0.569 9	0.560 9	C=1
	LMK-OC-ELM-sig	0.605 9	0.648 9	0.638 6	0.528 0	C=1
Diabetes	SVDD	0.686 0	0.601 7	0.710 4	0.590 3	σ=3.104 0
	PCA	0.667 5	0.572 6	0.693 2	0.560 7	n=6
	OC-SVM	0.662 6	0.575 8	0.684 7	0.565 1	γ=0.007
	OC-KELM	0.678 7	0.608 5	0.696 8	0.599 1	C=0.1, σ=2.249 2
	LMKAD	0.672 1	0.614 7	0.684 3	0.610 0	-
	LMK-OC-ELM-so	0.690 8	0.607 4	0.715 6	0.596 0	C=0.1
	LMK-OC-ELM-r	0.692 0	0.607 1	0.717 7	0.595 5	C=0.1
	LMK-OC-ELM-sig	0.702 3	0.627 6	0.722 8	0.623 6	C=0.01
Liver	SVDD	0.414 1	0.527 0	0.474 5	0.394 3	σ=2.817 9
	PCA	0.422 1	0.531 7	0.490 1	0.377 9	n=5
	OC-SVM	0.411 7	0.508 2	0.487 6	0.330 0	γ=0.012 6
	OC-KELM	0.421 0	0.522 6	0.497 6	0.344 3	C=0.01, σ=1.878 6
	LMKAD	0.419 9	0.530 1	0.486 4	0.380 5	-
	LMK-OC-ELM-so	0.422 2	0.524 2	0.498 9	0.345 8	C=0.001
	LMK-OC-ELM-r	0.421 5	0.527 4	0.493 2	0.362 9	C=0.01
	LMK-OC-ELM-sig	0.429 0	0.540 0	0.500 4	0.378 1	C=0.1
Spectf	SVDD	0.372 4	0.739 3	0.441 7	0.688 3	σ=9.644 2
	PCA	0.386 0	0.721 5	0.428 7	0.750 9	n=3
	OC-SVM	0.375 4	0.727 7	0.427 7	0.724 5	γ=0.013 1
	OC-KELM	0.370 5	0.752 9	0.453 6	0.658 1	C=0.01, σ=5.526 6
	LMKAD	0.414 4	0.779 3	0.461 0	0.723 4	-
	LMK-OC-ELM-so	0.413 2	0.747 0	0.457 1	0.768 9	C=1
	LMK-OC-ELM-r	0.404 8	0.741 8	0.450 7	0.756 8	C=0.1
	LMK-OC-ELM-sig	0.426 9	0.752 5	0.467 3	0.778 7	C=0.1
Abalone	SVDD	0.449 8	0.659 3	0.453 6	0.744 1	σ=3.672 1
	PCA	0.478 9	0.715 5	0.544 6	0.597 1	n=7
	OC-SVM	0.465 1	0.700 0	0.531 3	0.580 7	γ=0.010 8
	OC-KELM	0.452 7	0.686 0	0.520 3	0.563 1	C=1, σ=1.419 9
	LMKAD	0.500 8	0.736 4	0.561 0	0.632 0	-
	LMK-OC-ELM-so	0.525 6	0.732 2	0.548 4	0.730 9	C=10^-5
	LMK-OC-ELM-r	0.462 9	0.697 0	0.528 0	0.581 1	C=0.000 1
	LMK-OC-ELM-sig	0.506 4	0.737 1	0.562 2	0.643 3	C=10^-5
Breast	SVDD	0.882 2	0.894 7	0.888 4	0.898 6	σ=0.941 8
	PCA	0.873 4	0.876 6	0.873 5	0.876 2	n=7
	OC-SVM	0.924 4	0.929 0	0.925 6	0.930 7	γ=0.031 6
	OC-KELM	0.934 9	0.939 0	0.936 9	0.941 2	C=0.1, σ=0.638 9
	LMKAD	0.937 8	0.941 4	0.939 6	0.943 6	-
	LMK-OC-ELM-so	0.945 1	0.948 2	0.946 6	0.950 0	C=0.000 1
	LMK-OC-ELM-r	0.925 0	0.930 3	0.927 6	0.932 9	C=0.000 1
	LMK-OC-ELM-sig	0.945 8	0.948 8	0.947 2	0.950 7	C=10^-6

Table 2

Fig.1

Fig.2

Fig.3

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Fig.5

Table 3

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Fig.7

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算法	F1	AUC	G-mean	准确率	平均训练时间/s	平均测试时间/s
SVDD	0.922 2±0.030 7	0.885 4±0.028 8	0.923 8±0.028 8	0.886 5±0.039 8	0.031 4	0.006 9
PCA	0.918 5±0.020 9	0.912 6±0.018 2	0.921 0±0.019 7	0.883 5±0.028 0	0.060 7	0.004 4
OC-SVM	0.922 8±0.028 6	0.898 1±0.020 5	0.924 4±0.026 8	0.888 1±0.037 3	-	-
OC-KELM	0.925 0±0.019 2	0.930 5±0.014 9	0.927 8±0.018 7	0.893 1±0.029 9	0.001 7	0.001 5
LMKAD	0.930 1±0.023 7	0.933 8±0.021 4	0.932 5±0.022 3	0.900 0±0.031 5	-	-
LMK-OC-ELM-so	0.934 8±0.020 8	0.935 5±0.019 3	0.936 8±0.019 4	0.906 2±0.030 2	0.142 0	0.006 3
LMK-OC-ELM-sig	0.946 1±0.024 4	0.949 5±0.021 3	0.947 8±0.022 8	0.922 3±0.035 4	0.181 0	0.006 9

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Fault detection method for avionics based on LMKL and OC-ELM

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数据集	特征数	目标类数	异常类数	目标类名称
Ecoli	7	52	284	Periplasm
Liver	6	145	200	Healthy
Sonar	60	111	97	Mines
Diabetes	8	500	268	Present
Breast	9	458	241	Benign
Abalone	8	1 407	2 770	Classes 1-8
Spectf	44	55	212	0