故障响应指导下基于测试集的故障诊断方法

doi:10.13229/j.cnki.jdxbgxb20200059

Abstract

Abstract:

In this paper， having deeply studied the fault output characteristics in circuits and the ADD candidate diagnosis solution method， we propose a candidate diagnosis solution method under the guidance of fault response， ALFDD， namely. We also put forward the concept of candidate single fault set， which is based on the principle that a single fault with different actual output response and expected output response under current test excitations is more likely to be a fault diagnosis solution. To overcome the problem that in ADD method to obtain Fsame all single faults of circuit should be compared， we give the method which only considers the faults of the candidate single fault set to obtain Fsame. This method has three merits. Firstly， it does not need to compare all single faults and improves the efficiency of solving problems. Secondly， it eliminates the reluctant candidate solutions concluded in Fsame effectively， so it improves the resolution of candidate diagnostic solutions. In addition， this method improves the accuracy of candidate diagnostic solutions， namely， increasing the number of real diagnostic solutions in the candidate fault sets. The test results show that，， compared with ADD， the proposed ALFDD method increases the resolution， accuracy and solution efficiency for selecting candidate diagnoses.

Key words: computer application, model fault, fault diagnosis, candidate single fault set, accuracy of diagnosis, resolution of diagnosis

CLC Number:

TP391

Dan-tong OUYANG,Yang LIU,Jie LIU. Fault diagnosis method based on test set under fault response guidance[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1017-1025.

Figures/Tables 4

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References 16

1	Liu Meng, Ouyang Dan-tong, Cai Shao-wei, et al. Efficient zonal diagnosis with maximum satisﬁability[J]. Science China Information Sciences, 2018, 61(11):17-30.
2	Liu Meng, Ouyang Dan-tong, Zhang Li-ming. A novel approach for improving quality of health state with difference degree in circuit diagnosis[J]. Applied Intelligence, 2018, 48(11): 4371-4381.
3	刘梦,欧阳丹彤,刘伯文,等. 结合问题特征的分组式诊断方法[J]. 电子学报, 2018, 46(3): 589-594.
	Liu Meng, Ouyang Dan-tong, Liu Bo-wen, et al. Grouped diagnosis approach using the feature of problem[J]. Acta Electronica Sinica, 2018, 46(3): 589-594.
4	Wang Yi-yuan, Li Rui-zhi, Zhou Yu-peng, et al. A path cost-based GRASP for minimum independent dominating set problem[J]. Neural Computing & Applications, 2018, 28(1): 143-151.
5	刘思光,欧阳丹彤,张立明. 极小碰集求解中候选解极小性判定方法[J]. 软件学报, 2018, 29(12): 3733-3746.
	Liu Si-guang, Ouyang Dan-tong, Zhang Li-ming. Method of minimality-checking of candidate solution for minimal hitting set algorithm[J]. Journal of software, 2018, 29(12): 3733-3746.
6	赵剑,欧阳丹彤,王晓宇,等. 基于模型的混杂系统中控制器故障诊断方法[J]. 吉林大学学报:工学版, 2011, 41(1):155-159.
	Zhao Jian, Ouyang Dan-tong, Wang Xiao-yu, et al. Model-based diagnosis of controller fault in hybrid system[J]. Journal of Jilin University(Engineering and Technology Edition), 2011, 41(1):155-159.
7	Adapa R, Tragoudas S, Michael M K. Accelerating diagnosis via dominance relations between sets of faults[C]∥IEEE Vlsi Test Symposium, Berkeley, 2007: 219-224.
8	刘红,孙爽滋,王庆元,等. 基于PSO的模拟电路故障信息特征提取[J]. 吉林大学学报:工学版, 2015, 45(2): 675-680.
	Liu Hong, Sun Shuang-zi, Wang Qing-yuan, et al. PSO based feature extraction method for analog circuit fault information[J]. Journal of Jilin University(Engineering and Technology Edition), 2015, 45(2): 675-680.
9	Lavo D B, Chess B, Larrabee T, et al. Probabilistic mixed-model fault diagnosis[C]∥IEEE International Test Conference, Washington, 1998:1084-1093.
10	Venkataraman S, Drummonds S B. POIROT: a logic fault diagnosis tool and its applications[C]∥International Test Conference, Atlantic City, 2002:253-262.
11	Holst S, Wunderlich H J. Adaptive debug and diagnosis without fault dictionaries[J]. Journal of Electronic Testing, 2009, 25(4/5): 259-268.
12	Tang X, Cheng W T, Guo R, et al. Diagnosis of multiple physical defects using logic fault models[C]∥IEEE Asian Test Symposium, Shanghai, China, 2010:94-99.
13	Pomeranz I. OBO: an output-by-output scoring algorithm for fault diagnosis[C]∥IEEE Computer Society Annual Symposium on VLSI, Tampa, 2014:314-319.
14	Pomeranz I. Improving the accuracy of defect diagnosis by considering fewer tests[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2014, 33(12): 2010-2014.
15	Brglez F, Bryan D, Kozminski K. Combinational profiles of sequential benchmark circuits[C]∥Proceedings of IEEE International Symposium on Circuits and Systems, Portland, 1989: 1929-1934.
16	Brglez F, Fujiwara H. A neutral netlist of 10 combinational benchmark circuits and a target translator in fortran[C]∥Proceedings of IEEE International Symposium on Circuits and Systems, Kyoto, 1985:695-698.

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i	R_obs	R₀	R₁
0	0101	0001	0111
1	1010	1000	1100
2	0101	1010	1110
3	1101	0001	0001
4	0101	0101	0101
5	0110	1110	1110
6	1001	1000	1000
7	1011	1010	1010
8	1111	1110	1110
9	0101	0001	1100

电路名称	实际故障	模型故障	Overlap		Reso			Prec			rtime
电路名称	实际故障	模型故障	ADD	ALFDD	ADD	ALFDD	dval	ADD	ALFDD	dval	rtime
c17	5	14	2.3	4.2	0.67	0.80	0.13	0.46	0.84	0.38	0.88
c432	20	86	5.1	7.2	0.29	0.32	0.03	0.26	0.36	0.10	0.86
c499	20	146	7.8	8.0	0.35	0.44	0.09	0.39	0.40	0.01	0.53
c880	20	165	15.1	17.3	0.60	0.61	0.01	0.76	0.87	0.11	0.64
c1355	20	146	11.2	11.2	0.56	0.70	0.14	0.56	0.56	0.00	0.52
c1908	20	116	8.8	15.6	0.42	0.45	0.03	0.44	0.78	0.34	0.57
c2670	20	589	17.3	19.4	0.68	0.70	0.02	0.87	0.97	0.10	0.70
c3540	20	144	13.0	15.1	0.37	0.40	0.03	0.65	0.76	0.11	0.69
c5315	20	596	19.6	19.8	0.94	0.99	0.05	0.98	0.99	0.01	0.64
c6288	20	128	12.6	13.8	0.41	0.50	0.09	0.63	0.69	0.06	0.52

电路名称	实际故障	模型故障	Overlap		Reso			Prec			rtime
电路名称	实际故障	模型故障	ADD	ALFDD	ADD	ALFDD	dval	ADD	ALFDD	dval	rtime
s27	10	32	3.5	3.6	0.43	0.60	0.17	0.35	0.36	0.01	0.71
s208_1	20	217	8.6	9.8	0.27	0.28	0.01	0.43	0.49	0.06	0.98
s298	20	308	12.2	14.7	0.20	0.24	0.04	0.61	0.74	0.13	0.76
s344	20	342	7.2	8.4	0.33	0.38	0.05	0.36	0.42	0.06	0.74
s349	20	350	8.2	14.2	0.26	0.28	0.02	0.41	0.71	0.30	0.71
s382	20	399	12.3	13.1	0.22	0.24	0.02	0.62	0.66	0.04	0.75
s386	20	384	9.6	15.4	0.10	0.11	0.01	0.48	0.77	0.29	0.88
s400	20	424	6.8	11.8	0.09	0.10	0.01	0.34	0.59	0.25	0.76
s420	20	430	12.0	12.4	0.29	0.34	0.05	0.60	0.62	0.02	0.83
s420_1	20	455	9.6	10.0	0.32	0.26	-0.06	0.48	0.50	0.02	0.77
s444	20	474	13.4	15.7	0.28	0.29	0.01	0.67	0.79	0.12	0.77
s510	20	564	4.6	9.9	0.06	0.06	0.00	0.23	0.50	0.27	0.80
s526	20	555	9.3	11.4	0.18	0.20	0.02	0.47	0.57	0.10	0.76
s526n	20	553	9.1	10.1	0.17	0.20	0.03	0.46	0.51	0.05	0.75
s641	20	463	8.5	11.6	0.50	0.52	0.02	0.43	0.58	0.15	0.72
s713	20	581	16.2	16.5	0.21	0.22	0.01	0.81	0.83	0.02	0.72
s820	20	850	6.0	8.0	0.04	0.05	0.01	0.30	0.40	0.10	0.81
s832	20	870	10.0	10.1	0.06	0.07	0.01	0.50	0.51	0.01	0.81
s838	20	857	7.1	8.0	0.29	0.33	0.04	0.36	0.40	0.04	0.83
s838_1	20	931	9.5	14.6	0.19	0.21	0.02	0.48	0.73	0.26	0.79
s953	20	1033	9.0	12.1	0.16	0.17	0.01	0.45	0.61	0.16	0.82
s1196	20	1240	8.5	13.4	0.15	0.17	0.02	0.43	0.67	0.24	0.84
s1238	20	1353	8.7	12.3	0.07	0.08	0.01	0.44	0.62	0.18	0.81
s1423	20	1515	18.1	19.6	0.20	0.22	0.02	0.91	0.98	0.08	0.71
s1488	20	1486	6.7	8.9	0.02	0.02	0.00	0.34	0.45	0.11	0.79
s1494	20	1506	9.0	11.3	0.04	0.05	0.01	0.45	0.57	0.12	0.80

Fault diagnosis method based on test set under fault response guidance

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