The method of express-diagnostics of the roughness of the surface layer of machine parts on basis of a probabilistic model with hidden states

I. N Palamar; S. S Julin

doi:10.17816/2074-0530-67651

Izvestiya MGTU MAMI ›› 2014, Vol. 8 ›› Issue (1-2) : 27 -33. DOI: 10.17816/2074-0530-67651

Articles

research-article

The method of express-diagnostics of the roughness of the surface layer of machine parts on basis of a probabilistic model with hidden states

Author information +

History +

PDF

Abstract

The article suggests a method of express-diagnostics of the roughness of the surface layer of machine parts and shows its efficiency. The authors developed a graphical probabilistic model, providing high quality of diagnosis with small number of statistical data. Express diagnostics performs determination of the quality of surface layer on basis of profilogram classification. The diagnostic process is running as the comparison of profilograms with prepared models of different classes of roughness using statistical sampling. Program realization of the model is developed, specialists were trained and experimentally investigated the accuracy of the diagnostics.

Keywords

roughness / express-diagnostics / graphical probabilistic model

Cite this article

Download citation ▾

I. N Palamar, S. S Julin. The method of express-diagnostics of the roughness of the surface layer of machine parts on basis of a probabilistic model with hidden states. Izvestiya MGTU MAMI, 2014, 8(1-2): 27-33 DOI:10.17816/2074-0530-67651

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Andreao, R.V. ECG signal analysis through hidden Markov models / R.V. Andreao, В. Dorizzi, J. Boudy // Biomedical Engineering, IEEE Transactions. - 2006. - Vol. 53, Iss. 8. - P. 1541 - 1549.

[2]	Bishop, C. Pattern Recognition and Machine Learning. / C. Bishop. - New York : Springer, 2006. - 738 p.

[3]	Liang, P. An asymptotic analysis of generative, discriminative, and pseudo-likelihood estimators/ P. Liang, M. I. Jordan // In Proceedings of the 25th International Conference on Machine Learning (ICML). - 2008.

[4]	Rohlicek, J. R. Continuous hidden markov model for speaker independent word spotting/ J. R. Rohlicek, W. Russell, S. Roukod, H. Gish // International Conference on Audio, Speech and Signal Processing. - 1989. - Vol. 1 - р. 627 - 630.

[5]	Xue, J.-H. Comment on "discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes" / J.-H. Xue, D.M. Titterington // Neural Processing Letters. - 2008. - Vol. 28, Iss. 3. - P. 169 - 187.

[6]	Yun-Hsuan Sung, Boulis C., Manning C., Jurafsky D. Regularization, adaption, and non-independent features improve hidden conditional random fields for phone classification. // Automatic Speech Recognition & Understanding. Kyoto. 2007. P. 347 - 352.

[7]	Исследование эксплуатационной повреждаемости лопаток компрессора ГТД и разработка технологии их ремонта на основе упрочняющих методов обработки: автореф. дис. канд. техн. наук / Румянцева Н.В.: - Рыбинск, 2009.

[8]	Поверхностный слой и эксплуатационные свойства деталей машин/ А. М. Сулима, Ю. Д. Шулов: М.: Машиностроение, 1988. - 240 с.

[9]	Приборы неразрушающего контроля Techno-NTD. Измерители шероховатости, профилометры [Электронный ресурс]/ Режим доступа: http://t-ndt.ru/index.php?id=65.

[10]	Проведение экспериментальных исследований образцов, выполненных из материала ЭП 612, на машине трения T-11/ В. Ф. Безъязычный, M. Щерек, А. Н. Сутягин: Вестник РГАТУ имени П.А. Соловьева. Рыбинск, 2013. - № 1 - с. 84 - 90.