Diagnosability Design Method Based on Fault-sensitive Learning and Integrated Learning

doi:10.12382/bgxb.2023.0413

Abstract

Abstract:

The diagnosability design of system can optimize the diagnostic scheme and improve the the degree of compatibility between diagnostic scheme and system, which is of great significance for the accurate detection or isolation of system faults. For this, a diagnosability design method based on fault-sensitive learning and integrated learning is proposed. The featuresfrom the collected signals of system test points in different states are extracted. Combined with the results of diagnosability evaluation of the system, a quantitative feature contribution algorithm is proposed to evaluate the contribution degree of different features in the signal to fault diagnosis. The improved D-S evidence theory algorithm is used to fuse the features of different signals, which can determine the fault sensitive feature set suitable for fault detection and isolation. The integrated learning method is used to strengthen the diagnostic effect of base classifier, and finally the diagnosis scheme of the current system is obtained. Simulation experiments show that the system diagnosis scheme designed by the proposed diagnosability design method can make a good diagnosis of system faults. Compared with no fault-sensitive learning, the error rate of fault diagnosis decreases from 5.33% to 2.66%. Compared with no integrated learning, the error rate of fault diagnosis decreases from 16.22% (mean value of base diagnostics) to 2.66%. In the comparison experiments with other diagnostic schemes, the fault diagnosis error rate of the proposed method is decreased by 3.34% compared to the average fault diagnosis error rateof other methods.

Key words: diagnosability design, fault sensitivity, integratedlearning, adaptive boosting

CLC Number:

TP306⁺.3

LÜ Jiapeng, SHI Xianjun, WANG Yuanxin. Diagnosability Design Method Based on Fault-sensitive Learning and Integrated Learning[J]. Acta Armamentarii, 2024, 45(7): 2451-2462.

Figures/Tables 19

Fig.1 Schematic diagram of knowledge-based fault diagnosis method

Fig.2 Fault diagnosability design diagram

Fig.3 Flow chart of fault-sensitive learning method

Fig.4 Diagram of OVA architecture classification

Fig.5 Schematic diagram of classifier integrated learning

Fig.6 Circuit diagram

Table 1 Circuit fault setting

故障	元件	故障值
f₀	-
f₁	C₁/nf	10
f₂	R_s/kΩ	50
f₃	R_1s/kΩ	短路
f₄	R_1s/kΩ	20

Table 2 Types of signal characteristics

序号	信号特征	表达式
1	最大值	S_max= $m a x i$ $\mathscr{S}$ (i)
2	最小值	S_min= $m i n i$ $\mathscr{S}$ (i)
3	平均值	S_ave= $1 N ∑ i = 1 N$ $\mathscr{S}$ (i)
4	均方根	S_RMS= $1 N ∑ i = 1 N S (i) 2$
5	峰峰值	S_peak=S_max-S_min
6	峰值因子	S_peakF=S_peak/S_rms
7	峭度	S_kurtosis= $1 N ∑ i = 1 N (S (i) - S a v e) 4 [1 N ∑ i = 1 N (S (i) - s a v e) 2] 2$
8	偏度	S_skewness= $1 N ∑ i = 1 N (S (i) - S a v e) 4 [1 N ∑ i = 1 N (S (i) - s a v e) 2] 32$
9	波形因子	S_wave_F=S_RMS/( $1 N ∑ i = 1 N$ \| $\mathscr{S}$ (i)\|)
10	脉冲因子	S_impulseF=S_peak/( $1 N ∑ i = 1 N$ \| $\mathscr{S}$ (i)\|)
11	裕度因子	S_clearF=S_peak/( $1 N$ ( $∑ i = 1 N \| S (i) \|$ )²)

Table 2 Types of signal characteristics

序号	信号特征	表达式
1	最大值	S_max= $m a x i$ $\mathscr{S}$ (i)
2	最小值	S_min= $m i n i$ $\mathscr{S}$ (i)
3	平均值	S_ave= $1 N ∑ i = 1 N$ $\mathscr{S}$ (i)
4	均方根	S_RMS= $1 N ∑ i = 1 N S (i) 2$
5	峰峰值	S_peak=S_max-S_min
6	峰值因子	S_peakF=S_peak/S_rms
7	峭度	S_kurtosis= $1 N ∑ i = 1 N (S (i) - S a v e) 4 [1 N ∑ i = 1 N (S (i) - s a v e) 2] 2$
8	偏度	S_skewness= $1 N ∑ i = 1 N (S (i) - S a v e) 4 [1 N ∑ i = 1 N (S (i) - s a v e) 2] 32$
9	波形因子	S_wave_F=S_RMS/( $1 N ∑ i = 1 N$ \| $\mathscr{S}$ (i)\|)
10	脉冲因子	S_impulseF=S_peak/( $1 N ∑ i = 1 N$ \| $\mathscr{S}$ (i)\|)
11	裕度因子	S_clearF=S_peak/( $1 N$ ( $∑ i = 1 N \| S (i) \|$ )²)

Table 3 Fault detectability evaluation results

故障可检测性和测点	故障
故障可检测性和测点	f₁	f₂	f₃	f₄
故障可检测性	0.4207	0.3120	0.1871	0.5884
测点	测点3	测点3	测点6	测点6

Table 4 Fault isolability evaluation results

故障	f₁	f₂	f₃	f₄
f₁	0	0.7327	0.4207	0.9664
f₂	测点3	0	0.4679	0.3204
f₃	测点3	测点6	0	0.7751
f₄	测点6	测点6	测点6	0

Fig.7 Contribution of signal characteristics to fault diagnosis

Table 5 Diagnosis results of diagnostic algorithm

故障及合计	f₀	f₁	f₂	f₃	f₄	合计
诊断错误率/%	0	6.67	0	6.67	0	2.66

Table 6 Diagnostic results of different diagnosers %

故障及合计	基分类器1	基分类器2	基分类器3	基分类器4	基分类器5	基分类器6	集成分类器
f₀	0	13.33	26.67	0	6.67	80	0
f₁	0	0	26.67	6.67	13.33	100	6.67
f₂	6.67	13.33	33.33	0	0	66.67	0
f₃	6.67	6.67	13.33	0	6.67	13.33	6.67
f₄	0	0	20	6.67	6.67	13.33	0
合计	2.66	6.67	24	2.66	6.67	54.7	2.66

Fig.8 Comparison of diagnostic effects of base diagnoser and integrated diagnose

Table 7 Effects of different feature combinations on integrated diagnosis results %

序号	特征组合	f₀	f₁	f₂	f₃	f₄	合计
1	1 2 3 4 5 6	0	6.67	0	6.67	0	2.66
2	1 2	40	6.67	20	13.33	0	16
3	4 5 6	0	0	6.67	6.67	6.67	4
4	4 5	13.33	6.67	26.67	20	6.67	14.67
5	4	20	0	6.67	13.33	6.67	9.33
6	5	53.3	33.33	53.3	53.3	20	42.67
7	1 2 4 5	40	6.67	20	20	0	17.33
8	1 2 3 4 5 6 7 8 9 10 11	6.67	0	6.67	13.33	0	5.33
9	7 8	46.67	20	80	80	0	45.33
10	9 10 11	66.67	33.33	73.33	53.3	6.67	46.67
11	7 8 9 10 11	33.33	26.67	66.67	73.33	6.67	41.33

Table 8 Diagnostic results of different diagnosis schemes %

方法	f₁	f₂	f₃	f₄	合计
本文方法	6.67	0	6.67	0	2.66
文献[25]方法	0	26	13.33	0	8
文献[26]方法	0	0	13.33	6.67	4

Fig.9 Comparison of data distributions of multiple experiments in integrated learning sessions

Fig.10 Comparison of the data distributions of three methods after several experiments

Fig.11 Diagnostic results of three methods with different training to test sample ratios

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