Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2018, Article ID 9714206, 11 pages
https://doi.org/10.1155/2018/9714206
Research Article

Test Point Selection Method for Analog Circuit Fault Diagnosis Based on Similarity Coefficient

1School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, 37 Xueyuan Road, Beijing 100191, China
2School of Physics and Electronic Information, Huaibei Normal University, 100 Dongshan Road, Huaibei 235000, China

Correspondence should be addressed to Qingfeng Ma; moc.qq@efiqam

Received 17 September 2017; Revised 20 December 2017; Accepted 8 January 2018; Published 4 February 2018

Academic Editor: Konstantinos Karamanos

Copyright © 2018 Qingfeng Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The demand for testability analysis has increased with the integration densities and complexity of circuits. As an important part of testability analysis, the test point selection method needs to be researched in depth. A new similarity coefficient criterion is proposed to determine the fault isolation degree because output responses of a circuit with component tolerance are approximately subject to the normal distribution. Then, a new test point selection method is proposed based on the fault-pair similarity coefficient criterion information table. Simulation experiments are used to validate the accuracy of the proposed method in terms of the optimum test point set and fault isolation degree. The results show that the proposed method improves the performance of test point selection by comparing with the other reported methods.

1. Introduction

Testability analysis is an important research topic for fault diagnosis in analog circuits. It performs test generation and test point selection in order to improve observability of a circuit under test (CUT). Test generation technique is used to gain the optimal test excitation signals for a CUT and test point selection technique is used to search the optimal test point set for a CUT. This paper mainly studies the test point selection method.

Generally, a CUT often includes many test points, but not every test point in the CUT is necessary or measurable. The optimal test point selection technique can reduce the fault dictionary dimension and save the computational cost by eliminating redundant and immeasurable test points. The total test cost of the CUT can be reduced greatly. Although the exhaustive search method can select a global minimum test point set, the papers [13] pointed out that the method is NP-hard and only suitable for small scale analog systems because of its high computational cost. Now, test point selection for analog circuit has become very difficult due to the high dense packages of chips and complex electronic system. The compromise approach is to find a local minimum test point set [1, 3].

There have been many researches about test point selection methods in the past years. A heuristic method for test points selection based on the concept of confidence levels was proposed in paper [4]. The concept of ambiguity sets and developed logical rules to select test points was proposed by Hochwald and Bastian [5]. Lin and Elcherif [6] proposed two heuristic methods based on the criteria proposed by Hochwald and Bastian. Van Spaandonk and Kevenaar [7] combined the decomposition method of system sensitivity matrix and an iterative algorithm to search a set of test points for analog circuits. Prasad and Babu [8] proposed four algorithms based on inclusive approaches and exclusive approach. An entropy index method was proposed to search for a local minimum test point set [1]. A genetic algorithm was proposed to determine the optimal test point set, which effectively enabled the results to avoid being trapped into local minimums [9]. In paper [10], the test point selection procedure was transformed into a graph node expanding procedure and utilized entropy of information to guide graph search, and the method is subsequently improved by Gao et al. [11]. A greedy randomized adaptive search algorithm was proposed to find the global optimal test point set [12]. A multiobjective fruit fly optimization algorithm was proposed to enhance the global test point selection ability [13]. All test point selection methods reported above are based on an integer-coded dictionary technique.

Generally, analog CUT output values change in intervals because an analog signal has continuity and parameters of analog component have tolerances. Hence, the responses of some analog circuit faults usually overlap each other. In order to discriminate these ambiguous faults and construct an integer-coded dictionary for analog circuit, the ambiguity set and the ambiguity gap (i.e., a diode drop 0.7 V) between ambiguity sets were introduced by Hochwald and Bastian firstly [5]. Subsequently, most of test point selection algorithms employ 0.7 V as the ambiguity gap, but it is proved by many practical test results that 0.7 V is not always effective and accurate [2, 3]. In paper [14], 0.2 V was chosen as the ambiguity gap and an accurate fault-pair Boolean table technique for the test point selection was proposed, which overcame the shortcoming of the traditional integer-coded table that only a part of the faults can be isolated. In paper [2], ambiguity gaps were determined by means and variances of the circuit responses because the tolerances of component parameters obey the normal distribution. According to the normal distribution characteristics, an overlapped area method was proposed to improve the accuracy of selecting the optimal test point set. Subsequently, the fault-pair isolation table technique was proposed by Zhao and He to guide the test point selection algorithm. The table consisted of the isolation probability of fault-pairs, which was gained by calculating ambiguity gap [3].

This paper proposes a similarity coefficient criterion to compute each fault-pair isolation capability. The larger the coefficient is, the higher the isolation probability of a fault-pair is. According to the trait of a similarity coefficient criterion, the fault-pair similarity coefficient criterion information table is constructed and a new test point selection method is proposed. And then the proposed method is validated by two filter benchmark circuits in terms of the optimal test point set and fault isolation degree. The results show that the new method is effective and accurate.

The remainder of this paper is arranged in the following order. Section 2 introduces the new method to construct the fault-pair similarity coefficient criterion information table. Then a new test point selection algorithm is proposed based on the table. The simulation details are described and the simulation results are discussed in Section 3. In the end, brief conclusions are summarized in Section 4.

Nomenclature of the paper is listed in Nomenclature.

2. New Test Point Selection Method

2.1. Similarity Coefficient Criterion

The core idea of the similarity coefficient criterion is to estimate the overlapping degree of a fault-pair. Assume that there are two faults and , and their samples follow the normal distribution. The probability density function curves are and , respectively. Three pairs of fault curves with different overlapping degree are shown in Figure 1. Because and are one-dimensional nonnegative real functions, they satisfy the condition of Cauchy-Schwarz inequality. The following inequality can be deduced according to Cauchy-Schwarz inequality [15]:The similarity coefficient criterion is defined as the following mathematical formula: so its interval is from 0 to 1. If the curves and almost coincide with each other as shown in Figure 1(a), and cannot be isolated and . If the curves and are partially overlapping as shown in Figure 1(b), faults and cannot be isolated partially and . If the curves and are not overlapping absolutely as shown in Figure 1(c), faults and can be isolated completely and . For example, assume that the mean and variance of fault are 3.32 and 0.24; the probability density function of fault is expressed asassume that the mean and variance of fault are 4.33 and 0.13:According to formula (2), the calculation of is 0.999. If is rounded to 2 decimal places, and the fault-pair can be isolated.

Figure 1: Probability density function curves and .
2.2. Fault-Pair Similarity Coefficient Criterion Information Table

According to Luo’s method [2], a fault dictionary is constructed with the mean and standard variance values of fault samples. If the fault dictionary is composed of fault modes and test points, the proposed fault-pair similarity coefficient criterion information table has rows of fault-pairs on the basis of combination formula and columns of test points. Assume that a test point of the th column is and the fault-pair of the th row is and in the table; the mean and standard variance of and samples of a CUT on the test point can be found in the th column of the fault dictionary. And then, the similarity coefficient criterion of the normal distribution curves and on the test point can be calculated by formula (2). Therefore, the data in cells of the table is similarity coefficients’ criterion of all fault-pairs. In order to enhance the accuracy and the efficiency of test point selection, two rows of information, and , and one column of information, , are added to the proposed table. is the sum of ’s which equals 1 in column and indicates that test point can isolate the total of fault-pairs. is the sum of ’s which are not equal to 1 and indicates the isolation capability of all overlapping fault-pairs in the column . is the total of 1’s in the th row and indicates all test points that can isolate the th fault-pair. Hence, the proposed fault-pair similarity coefficient criterion information table has rows and columns.

2.3. Criteria, Steps, and Complexity of the New Algorithm

The new algorithm is proposed based on the fault-pair similarity coefficient criterion information table. means only one test point isolates the fault-pair in the th row, so the test point is removed from the candidate test point set and added to the optimal test point set . If a test point having means that it can isolate the maximal number of fault-pairs, it is selected from and added to . If several test points have the same , ’s of these test points are compared and the test point with is added to . Based on the above criteria, steps of the new algorithm are given as follows.

Step 1. is initialized to a null set. It is constructed that a fault dictionary has rows and columns based on mean and standard variance of fault samples.

Step 2. A fault-pair similarity coefficient criterion information table based on the fault dictionary is constructed.

Step 3. Look up the fault-pair (i.e., row) that corresponds to ; the corresponding test point is added to because only it can distinguish the th fault-pair. The other fault-pairs (i.e., rows) isolated by the test point are deleted from the fault-pair similarity coefficient criterion information table, and then the test point (i.e., column) is also deleted from the table. Until all the fault-pairs and all the test points corresponding to have been dealt with completely, the algorithm goes to Step 5. If no equals 1, the algorithm goes to Step 4 directly.

Step 4. of each test point in the fault-pair similarity coefficient criterion information table is calculated, and the test point with is added to . If several test points have the same , their ’s are compared and the test point with is added to . After the corresponding fault-pairs and the corresponding test point are deleted from the table, the algorithm goes to Step 5.

Step 5. Check whether the selected test points can isolate all fault-pairs in the table or the remainder fault-pairs cannot be isolated anyhow. If yes, is outputted and the algorithm terminates; else, the algorithm goes back to Step 3.

The calculation of complexity of the new algorithm is similar to that of literature [3]. The result of complexity isthat is, , where is the number of test points that are selected into .

3. Experiment Circuit and Simulation Results

3.1. Experiment on the Leapfrog Filter Circuit

The simulation CUT is a leapfrog filter, which is a benchmark circuit of ITC97. The nominal value of each component is labeled in Figure 2 and the tolerances of resistor and capacitance are set as 5% and 10%, respectively. It is reported that single hard faults account for approximately 90% of all analog faults occurring in practice [3], so 16 single hard faults are examined on the simulation CUT. The normal mode is labeled as and all modes are listed in Table 1. 1 Ω resistor is used to represent the short circuit fault and 1 MΩ resistor is used to represent the open circuit fault in circuit simulation. All modes are simulated by OrCAD/PSPICE16.6 and 1 kHz; 5 V sinusoidal signal is chosen as the stimulus of circuit simulation. The number of runs is set to 50 and tolerance distribution is set as Gaussian in Monte Carlo analysis. Because each mode takes 50 samples by Monte Carlo analysis, all 600 sample datasets are obtained from 12 test points of the simulation CUT for each mode.

Table 1: Fault modes of the first CUT.
Figure 2: Schematic of a leapfrog filter circuit.

According to criteria and steps of the new algorithm, the algorithm firstly starts the initialized work. is initialized as and is initialized to a null set, and the fault dictionary as shown in Table 2 is constructed based on mean and standard variance of the test point voltage maximum values of fault samples.

Table 2: Fault dictionary of the first CUT (unit: V).

Secondly, the fault-pair similarity coefficient criterion information table is constructed based on the fault dictionary. Table 3 shows a part of the proposed table. The similarity coefficient equals 1 that indicates the fault-pairs can be isolated on the test point completely. If equals 0, the fault-pairs cannot be isolated on the test anyhow. If the value of is between 0 and 1, this indicates the fault-pairs can be isolated on the test point partly. For example, the 2nd fault-pair in Table 3 includes the three kinds of similarity coefficient values.

Table 3: Fault-pair similarity coefficient criterion information table of the first CUT.

Thirdly, the corresponding test points are added to because equals 1. After deleting the corresponding test points and the corresponding fault-pairs from the fault-pair similarity coefficient criterion information table, the size of the table as shown in Table 4 reduced greatly.

Table 4: Reduced fault-pair similarity coefficient criterion information table of the first CUT.

Fourthly, the end conditions of the algorithm cannot be satisfied, so it goes to Step 3. No equals 1, so the algorithm goes to Step 4 and calculates and of each remainder test point. After comparing ’s of the remainder test points, and are found to be the same and their values are 1. In order to select the better test point, and are compared. Because of is larger than of , is added to . After deleting and , the algorithm goes to Step 5.

Finally, no fault-pair can be isolated by the remainder test points, so the algorithm stops and the final is . The final fault-pair similarity coefficient criterion information table is shown in Table 5. The fault-pairs , , and (i.e., five faults ,, , , and ) cannot be isolated.

Table 5: Remainder fault-pair similarity coefficient criterion information table of the first CUT.
3.2. Comparison Experiment

The comparison experiment of accuracy is made between the proposed method and five reported methods. The interval parameter 1.96 is usually used to determine ambiguity gaps [2, 3]. According to the normal distribution theory, the probability is 95% when the interval parameter is 1.96. The same ambiguity gap (i.e., [] is adopted in the five reported methods for comparison under the same condition. The new method needs integral operation in the interval to solve , and then the threshold value of should be set to distinguish whether the fault-pair can be isolated or not. The threshold values 1.00 and 0.95 are set in new method to compare with other methods. The comparison results of these methods are listed in Table 6.

Table 6: Results of comparison experiment with the other reported methods.

As can be clearly seen from Table 6, three methods which adopt the integer-coded fault dictionary have the same fault isolation degree, but their fault isolation degree is smaller than those of other three methods which adopt fault-pair.

When the threshold value of the new method is set as 1.00, all the methods except Starzyk’s method obtain the same size of test points set. However, the new method and Yang’s method have the largest fault isolation degree. When the threshold value of the new method is set as 0.95, the new method has the smallest test point set. Therefore, the new method is accurate and effective.

3.3. Experiment with Different Threshold of on the Active Filter Circuit

The second simulation CUT is shown in Figure 3. A 1 kHz, 4 V sinusoidal signal is chosen as the stimulus of circuit simulation. The tolerances of resistor and capacitance, parameters of Monte Carlo analysis and fault mode setting method, are the same as the first CUT. 20 single hard faults and normal mode are tested on the CUT. All modes are listed in Table 7. According to samples of the active filter circuit, a fault dictionary is constructed and shown in Table 8.

Table 7: Fault modes of the second CUT.
Table 8: Fault dictionary of the second CUT (unit: V).
Figure 3: Schematic of active filter circuit.

is solved by integral operation in the interval according to formula (2). Because theoretically the probability density function curves of any fault-pair are overlapping in the interval and the value of is less than 1, the threshold value of should be set in practical application. The test results with different threshold value are shown in Table 9. The threshold value 1 is obtained by rounding it to 2 decimal places for . In Table 9, the larger threshold values have more fault-pairs that cannot be isolated because the overlapped area of probability density function curves increases. Under threshold values 0.9, 0.92, 0.94, 0.96, and 0.98 conditions, the fault isolation degrees and isolated fault modes are the same which illustrates that the increased fault-pairs that cannot be isolated originate from the same ambiguity fault set. However, the test point set of threshold value 0.9 is the smallest among these threshold values. can be selected as the optimal test point set to diagnose the CUT in practice.

Table 9: Test results with different threshold value of C’s for the second CUT.

4. Conclusion

This paper uses the similarity coefficient criterion to construct a fault-pair similarity coefficient criterion information table. According to the table, the optimal test point set is obtained. By analysis, the complexity of the algorithm is proved to be . The feasibility and validity of the proposed method have been verified by the simulation experiments, and the accuracy of the proposed method has been demonstrated by the comparison experiment with the other reported methods. The results indicate that the proposed method achieves a significant improvement in the optimal test point selection for analog circuit.

Nomenclature

The th fault
The th fault
Similarity coefficient criterion
Number of candidate test points
Number of all the faults
The th test point
Similarity coefficient criterion in the th row and column
Total number of 1’s in column
Sum of all overlapping fault-pair similarity coefficient criterion information in column
Total of 1’s in the th row
Optimal test point set
Candidate test point set.

Conflicts of Interest

The authors declare that they have no conflicts of interest in connection with the work submitted.

Acknowledgments

This work was supported by Natural Science Research Project of Universities in Anhui Province (no. KJ2017B010).

References

  1. J. A. Starzyk, D. Liu, Z.-H. Liu, D. E. Nelson, and J. O. Rutkowski, “Entropy-based optimum test points selection for analog fault dictionary techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 3, pp. 754–761, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Luo, Y. Wang, H. Lin, and Y. Jiang, “A new optimal test node selection method for analog circuit,” Journal of Electronic Testing, vol. 28, no. 3, pp. 279–290, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Zhao and Y. He, “A new test point selection method for analog circuit,” Journal of Electronic Testing: Theory and Applications, vol. 31, no. 1, pp. 53–66, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. K. C. Varghese, J. Hywel Williams, and D. R. Towill, “Computer aided feature selection for enhanced analogue system fault location,” Pattern Recognition, vol. 10, no. 4, pp. 265–280, 1978. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Hochwald and J. D. Bastian, “A DC approach for analog fault dictionary determination,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 26, no. 7, pp. 523–529, 1979. View at Publisher · View at Google Scholar · View at Scopus
  6. P. M. Lin and Y. S. Elcherif, “Analogue circuits fault dictionary—new approaches and implementation,” International Journal of Circuit Theory and Applications, vol. 13, no. 2, pp. 149–172, 1985. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Van Spaandonk and T. A. M. Kevenaar, “Iterative test-point selection for analog circuits,” in Proceedings of the 14th IEEE VLSI Test Symposium, pp. 66–71, New Jersey, USA, May 1996. View at Scopus
  8. V. C. Prasad and N. S. C. Babu, “Selection of test nodes for analog fault diagnosis in dictionary approach,” IEEE Transactions on Instrumentation and Measurement, vol. 49, no. 6, pp. 1289–1297, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Golonek and J. Rutkowski, “Genetic-algorithm-based method for optimal analog test points selection,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 54, no. 2, pp. 117–121, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Yang, S. Tian, and B. Long, “Application of heuristic graph search to test-point selection for analog fault dictionary techniques,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 7, pp. 2145–2158, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Gao, C. Yang, S. Tian, and F. Chen, “Entropy based test point evaluation and selection method for analog circuit fault diagnosis,” Mathematical Problems in Engineering, vol. 2014, Article ID 259430, 16 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Lei and K. Qin, “Greedy randomized adaptive search procedure for analog test point selection,” Analog Integrated Circuits and Signal Processing, vol. 79, no. 2, pp. 371–383, 2014. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. Ma, Y. He, and F. Zhou, “Multi-objective fruit fly optimization algorithm for test point selection,” in Proceedings of the 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2016, pp. 272–276, Xi’an, China, October 2016. View at Publisher · View at Google Scholar · View at Scopus
  14. C. Yang, S. Tian, B. Long, and F. Chen, “A novel test point selection method for analog fault dictionary techniques,” Journal of Electronic Testing, vol. 26, no. 5, pp. 523–534, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Zhang, L. Hu, and W. Jin, “Resemblance coefficient and a quantum genetic algorithm for feature selection,” Lecture Notes in Computer Science, vol. 3245, pp. 155–168, 2004. View at Google Scholar · View at Scopus