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

摘要/Abstract

摘要：

通过对电路中故障输出特征进行研究，结合增强故障诊断（ADD）测试候选诊断求解方法，提出了故障响应指导下的候选诊断求解方法ALFDD。依据当前测试激励下实际输出响应与预期输出响应不相同的单故障更可能是故障诊断解的原理，提出了候选单故障集合的概念。针对ADD方法中需对电路的所有单故障求解F_same的问题，给出仅对候选单故障集合求解F_same的方法。该方法一方面避免了对所有单故障都进行对比并求解F_same的问题，提高了诊断求解效率；另一方面有效删除了F_same中包含的冗余候选诊断解，提高了候选诊断解的分辨率；此外，还增加了候选故障集中所包含的真实诊断解的数量，提高了候选诊断解的准确率。实验结果表明，与ADD方法相比，ALFDD方法候选诊断分辨率和准确率明显提高，求解效率也有较大提高。

关键词: 计算机应用, 模型故障, 故障诊断, 候选单故障集合, 诊断准确性, 诊断分辨率

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

中图分类号:

TP391

欧阳丹彤,刘扬,刘杰. 故障响应指导下基于测试集的故障诊断方法[J]. 吉林大学学报(工学版), 2021, 51(3): 1017-1025.

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.

图/表 4

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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