UDK 519.6
DETECTION OF HOMEGENEOUS PRODUCTION BATCHES OF SPACE ELECTRONIC COMPONENTS BASED ON SEPARATION OF A MIXTURE OF SPHERICAL GAUSSIAN DISTRIBUTIONS
V. I. Orlov, D. V. Stashkov, L. A. Kazakovtsev, I. R. Nasyrov, A. N. Antamoshkin
Reshetnev Siberian State Aerospace University 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660037, Russian Federation
Separating of homogeneous production batches of the electronic components used in the electronic units of the space systems is one of the most important problems which must be solved for improving quality of such units, their lifetime and reliability of the space systems. The quality of the electronic units is increased due to both more coordinated work of the EEE components which have identical parameters and increase of quality level and the accuracy of the destructive tests due to a new opportunity of guaranteed selecting electronic elements for these destructive tests from each production batch. In this paper, we solve the problem of precipitations of homogeneous batches of industrial products using Gaussian spherical mixture models and the EM algorithm with agglomerative greedy heuristic procedure. The EM (Expectation Maximization) algorithm is an efficient means of splitting a mix of various distributions. However, in case of multi-dimensional Gaussian distributions in a space of very large dimensionality, this algorithm is actually unworkable. In case of large volume of input data, this algorithm demands too complicated calculation for rebuilding its correlation matrices at each iteration. In case of small data volume, algorithm leads to detection of fake correlation in data. In our paper, the shipped lot of the electronic components for space industry is represented by a data set of non-destructive test results which is considered as a mixture of spherical Gaussian distributions (SGD). It is shown that this algorithm allows to efficiently determine homogeneous products batches which are rather large (thousands units) using of high-dimensional array of data (up to some hundreds dimensions). We show that, using this mathematical model in combination with new algorithms is capable to separate the homogeneous batches of the electronic components efficiently and reach more accuracy and stability of results in comparison with random multiple start of the algorithm.
Keywords: electronic components reliability, clustering, fuzzy clustering.
References

1. Korolev V. Ju. EM-algoritm, ego modifikatsii i ikh primenenie k zadache razdeleniya smesey veroyatnostnykh raspredeleniy. Teoreticheskiy obzor [EM algorithm, its modifications and their application to the problem of division of mixes of probabilistic distributions]. Moscow, IPI RAN Publ., 2007, 97 p.

2. Cherezov D. S., Tyukachev N. A. [Review of the main methods of classification and clustering of data]. Vestnik VGU. Seriya: Sistemnyy analiz i informatsionnye tekhnologii. 2009, No. 2, P. 25–29 (In Russ.).

3. Kazakovtsev L. A., Orlov V. I., Stupina A. A. [On distance metric for the system of automatic classification of the EEE devices by production batches]. Programmnye produkty i sistemy. 2015, No. 2, P. 124–129. Doi: 10.15827/0236-235X.110.124-129 (In Russ.).

4. Fedosov V. V., Kazakovtsev L. A., Gudyma M. N. [Problem of normalization of source testing data of space EEE components clustering algorithm]. Informatsionnye tekhnologii modelirovaniya i upravleniya. 2016, No 4, P. 263–268 (In Russ.).

5. Orlov V. I., Stashkov D. V., Gudyma M. N., Kazakovtsev L. A. [EM-algorithm for problem of automatic grouping of electronic components]. Materialy XX Yubileynoy mezhdunarodnoy nauchno-prakticheskoy konferentsii “Reshetnevskie chteniya” [Proceed. of XX Anniversary International Scientific and Practical Conference “Reshetnev Readings”]. Krasnoyarsk, 2016, Vol. 2, P. 72–73 (In Russ.).

6. Dasgupta S., Schulman S. J. A Two-Round Variant of EM for Gaussian Mixtures. UAI’00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence. 2000, P. 152–159.

7. Varadhan S. R. S. Special invited paper: Large deviations. The Annals of Probability. 2008, Vol. 36, No. 2, P. 397–419. Doi:10.1214/07-AOP348.

8. Bishop C. Neural networks for pattern recognition. New York : Oxford University Press. 1995, 498 p.

9. Dasgupta S. Learning mixtures of Gaussians. IEEE Symposium on Foundations of Computer Science. 1999, P. 634–644.

10. Borg J. F. P. Modern Multidimensional Scaling: Theory and Application Springer. 2005, P. 207–212.

11. Fedosov V. V., Orlov V. I. [Minimal necessary extent of examination of microelectronic products at inspection test stage]. Izvestiya Vuzov. Priborostroenie. 2011, Vol. 54(4), P. 62–68 (In Russ.).

12. Kazakovtsev L. A., Orlov V. I., Stupina A. A. [Task of electronic components classifying]. Vestnik SibGAU. 2014, No. 4(56), P. 55–61 (In Russ.).

13. Fedosov V. V., Patraev V. E. [Increase in reliability of the radio-electronic equipment of spacecrafts at application of the EEE which have passed additional defects tests in the specialized testing technical centers]. Aviakosmicheskoe priborostroenie. 2006, No. 10, P. 50–55 (In Russ.).

14. Kalashnikov O. A. Nikiforov A. Yu. [Technique of certification of electronic component base of the onboard space equipment on resistance to dose influence]. Spetstekhnika i svyaz’. 2011, No. 4–5, P. 32–38 (In Russ.).

15. Kalashnikov O. A., Nekrasov P. V., Demidov A. A. [Functional control of microprocessors when carrying out radiation tests]. Pribory i tekhnika eksperimenta. 2009, No. 2, 48 p. (In Russ.).

16. Patraev V. E. Metody obespecheniya i otsenki nadezhnosti kosmicheskikh apparatov s dlitel’nym srokom aktivnogo sushchestvovaniya [Methods of providing and assessment of reliability of spacecrafts with the long term of active existence: monograph]. Krasnoyarsk, SibSAU Publ., 2010, 136 p.

17. Lloyd S. P. Least Squares Quantization in PCM. IEEE Transactions on Information Theory. 1982, Vol. 28, P. 129–137.

18. MacQueen J. B. Some Methods of Classification and Analysis of Multivariate Observations. Proceedings
 of the 5th Berkley Symposium on Mathematical Statistics and Probability. 1967, Vol. 1, P. 281–297. 19. Kazakovtsev L. A. [Determistic algorithm for k-means and k-medoids problems]. Sistemy upravleniya i informatsionnye tekhnologii. 2015, No. 1(59), P. 95–99 (In Russ.).

20. Kazakovtsev L. A., Antamoshkin A. N., Masich I. S. Fast deterministic algorithm for EEE components classification problems. IOP Conference Series: Materials Science and Engineering. 2015, Vol. 94. Article ID 012015. Doi: 10.1088/1757-899X/94/1/ 012015.

21. Kazakovtsev L. A., Stupina A. A., Orlov V. I. [Modification of genetic algorithm with greedy heuristics for continuous location and classification problems]. Sistemy upravleniya i informatsionnye tekhnologii. 2014, No. 2(56), P. 35–39 (In Russ.).

22. Kazakovtsev L. A., Orlov V. I., Stupina A. A., Kazakovtsev V. L. Modified Genetic Algorithm with Greedy Heuristic for Continuous and Discrete p-Median Problems. Facta Universitatis (Nis) Series Mathematics and Informatics. 2015, Vol. 30, No. 1, P. 89–106.

23. Kazakovtsev L. A. [Evolutionary algorithm for k-medoids problem]. Sistemy upravleniya i informatsionnye tekhnologii. 2015, No. 2(60), P. 36–40 (In Russ.).

24. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical. Learning Springer-Verlag. 2009, P. 764.

25. Gmurman V. E. Teoriya veroyatnostey i matematicheskaya statistika [Probability theory and mathematical statistics]. Moscow, Vysshee obrazovanie Publ., 2005, 400 р.

26. Decimal – Decimal fixed point and floating point arithmetic. Available at: URL https://docs.python.org/2/library/decimal.html (accessed: 01.12.2016).

27. Kazakovtsev L. A., Antamoshkin A. N. [Greedy heuristic method for location problems]. Vestnik SibGAU. 2015, Vol. 15, No. 2, P. 317–325 (In Russ.).

28. Orlov V. I., Stashkov D. V., Kazakovtsev L. A., Stupina A. A. Fuzzy clustering of EEE components for space industry. IOP Conference Series: Materials Science and Engineering. 2016, Vol. 155, Article ID 012026.


Orlov Viktor Ivanovich – Cand. Sc., doctorate student, Department of System Analysis and Operations Research,

Reshetnev Siberian State Aerospace University. E-mail: ttc@krasmail.ru.

Stashkov Dmitriy Viktorovich – computer programmer of JSC “Sinetic”. E-mail: stashkov@sinetic.ru.

Kazakovtsev Lev Aleksandrovich – Cand. Sc., Docent, Department of System Analysis and Operations Research,

Reshetnev Siberian State Aerospace University. E-mail: levk@bk.ru.

Nasyrov Ilnar Rafinatovich – postrgraduate student, Reshetnev Siberian State Aerospace University. E-mail:

nir76@mail.ru.

Antamoshkin Alexander Nikolaevich – Dr. Sc., professor, Department of System Analysis and Operations

Research, Reshetnev Siberian State Aerospace University. E-mail: oleslav@mail.ru.