Generate QR code
Open Access
Subscription Access
Neural networks application for phasechronometric measurement information processing
https://doi.org/10.32446/0368-1025it.2020-9-31-35
Abstract
This article describes the processing technique of measuring phasechronometric information based on the neural networks use. The novelty of the proposed approach lies in the choice of a classification feature and the perceptron algorithm use as an algorithm for binary classification performing. In this article, to assess the concept operability, the simplest binary classification of the lathe operation modes is made: idle or cutting.
About the Authors
D. D. Boldasov
Bauman Moscow State Technical University
Russian Federation
Russian Federation
Dmitriy D. Boldasov
Moscow
Ju. V. Drozdova
Bauman Moscow State Technical University
Russian Federation
Russian Federation
Julia V. Drozdova
Moscow
A. S. Komshin
Bauman Moscow State Technical University
Russian Federation
Russian Federation
Alexander S. Komshin
Moscow
A. B. Syritskii
Bauman Moscow State Technical University
Russian Federation
Russian Federation
Antony B. Syritskii
Moscow
References
1. Kabak I. S., Sukhanova N. V., Gadelev A. M., Izvestiya Kabardino-Balkarskogo gosudarstvennogo universiteta, 2012, no. 4, vol. 2, pp. 77–79 (in Russian).
2. Azmi A. I., Advances in Engineering Software, 2015, vol. 82, pp. 53–64. https://doi.org/10.1016/j.advengsoft.2014.12.010
3. Rizal M. et al., Applied Soft Computing, 2013, vol. 13, pp. 1960–1968. https://doi.org/10.1016/j.asoc.2012.11.043
4. Silva R. G., Reuben R. L., Baker K. J. and Wilcox S. J., Mechanical Systems and Signal Processing, 1998, vol. 12, pp. 319–332. https://doi.org/10.1006/mssp.1997.0123
5. Proteau A. et al., The International Journal of Advanced Manufacturing Technology, 2019, vol. 103, pp. 101–110. https://doi.org/10.1007/s00170-019-03533-4
6. Syritskii A. B., Measurement Techniques, 2016, vol. 59, no. 6, pp. 595–599. https://doi.org/10.1007/s11018-016-1014-1
7. Kiselev M. I., Pribory, 2016, no. 10 (196), pp. 51–54 (in Russian).
8. Boldasov D. D., Komshin A. S., Syritskii A. B., Method of Lathe Tool Condition Monitoring Based on the Phasechronometric Approach. In: Advances in Automation. RusAutoCon 2019, eds Radionov A., Karandaev A. Lecture Notes in Electrical Engineering, 2020, Springer, Cham, vol. 641. https://doi.org/10.1007/978-3-030-39225-3_82
9. Geurts P., Wehenkel L., Knowledge Discovery in Databases: PKDD 2005, Springer, 2005, рp. 478–485. https://doi.org/10.1007/11564126_48
10. Scikit-learn user guide Chapter 3 Supervised learning. Release 0.20.3 March 2019, available at: https://scikitlearn.org/stable/_downloads/scikit-learn-docs.pdf(accessed:06.03.2019).
Review
For citations:
Boldasov D.D., Drozdova J.V., Komshin A.S., Syritskii A.B. Neural networks application for phasechronometric measurement information processing. Izmeritel`naya Tekhnika. 2020;(9):31-35. (In Russ.) https://doi.org/10.32446/0368-1025it.2020-9-31-35
Views:
88
Email this article 