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Tytuł artykułu

Discriminant Analysis and Optimization Applied to Vibration Signals for the Quality Control of Rotary Compressors in the Production Line

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, the applications of the multivariate data analysis and optimization on vibration signals from compressors have been tested on the assembly line to identify nonconforming products. The multivariate analysis has wide applicability in the optimization of weather forecasting, agricultural experiments, or, as in this case study, in quality control. The techniques of discriminant analysis and linear program were used to solve the problem. The acceleration and velocity signals used in this work were measured in twenty-five rotating compressors, of which eleven were classified as good baseline compressors and fourteen with manufacturing defects by the specialists in the final acoustic test of the production line. The results obtained with the discriminant analysis separated the conforming and nonconforming groups with a significance level of 0.01, which validated the proposed methodology.
Słowa kluczowe
Rocznik
Strony
79--87
Opis fizyczny
Bibliogr. 28 poz., fot., rys., tab.
Twórcy
  • Department of Mechanics, Faculty of Mechanical Engineering, UFU Universidade Federal de Uberlândia, Al. João Naves de Ávila 2121, 38408-144 Uberlândia, Brazil
  • Department of Mechanics, Faculty of Mechanical Engineering, UFU Universidade Federal de Uberlândia, Al. João Naves de Ávila 2121, 38408-144 Uberlândia, Brazil
autor
  • Department of Mechanics, Faculty of Mechanical Engineering, UFU Universidade Federal de Uberlândia, Al. João Naves de Ávila 2121, 38408-144 Uberlândia, Brazil
  • Department of Mechanics, Faculty of Mechanical Engineering, UFU Universidade Federal de Uberlândia, Al. João Naves de Ávila 2121, 38408-144 Uberlândia, Brazil
Bibliografia
  • 1. Amarnath M. (2016), Local fault assessment in a helical geared system via sound and vibration parameters using multiclass SVM Classifiers, Archives of Acoustics, 41, 3, 559-571.
  • 2. ASHRAE Handbook (2008), Chapter 37 – Compressor, “HVAC Systems and Equipment” (SI).
  • 3. Barbosa W. A., Lenzi A., Zanin P. H. T. (2002), Vibratory energy flux study in a hermetic compressor by statistical energy analysis, Archives of Acoustics, 27, 1, 23-30.
  • 4. Barbosa W. A., Zanin P. H. T., Lenzi A. (2003), Parameter analysis of vibratory energy flow in a hermetic compressor by statistical energy analysis, Archives of Acoustics, 28, 3, 151-159.
  • 5. Carletti E. (2013), A perception-based method for the noise control of construction machines, Archives of Acoustics, 38, 2, 253-258.
  • 6. Carnero M. C., Gonzalez-Palma R., Almorza D., Mayorga P., Lopez-Escobar C. (2010), Statistical quality control through overall vibration analysis, Mechanical Systems and Signal Processing, 24, 1138-1160.
  • 7. D’Elia G., Delvecchio S., Malagò M., Dalpiaz G. (2014), On the use of vibration signal analysis for industrial quality control: Part I, [in:] Dalpiaz G. et al. [Eds.], Advances in condition monitoring of machinery in non-stationary operations. Lecture notes in mechanical engineering, Springer, Berlin, Heidelberg.
  • 8. Duarte J. (2013), Artificial Intelligence applied to quality control in production Lines, M.Sc. Dissertation, Uberlândia – MG, Universidade Federal de Uberlândia, Faculty of Mechanical Engineering.
  • 9. Duarte J. B., Duarte M. A. V., Fagundes Neto M. G. (2015), use of competitive neural network to acceptance of compressors in assembly line, International Journal of Emerging Technology and Advanced Engineering, 5, 512-420.
  • 10. Fagundes Neto M. G., Duarte M. A. V. (2015), Identification of sources and propagation paths of noise and vibration in rotary compressors, International Journal of Engineering and Innovative Technology, 4, 11-19.
  • 11. Ganapavarapu L. K., Prathigadapa S. (2015), Study on total quality management for competitive advantage in international business, Arabian Journal of Business and Management Review, 5, 3.
  • 12. Gerges S. N. Y. (2000), Noise fundamentals and control, Universidade Federal de Santa Catarina, Mechanical Engineering Department, Laboratory of Vibrations and Acoustics, NR Publishing company, Florianópolis.
  • 13. Khattree R., Naik D. N. (2000), Multivariate data reduction and discrimination with SAS software, Wiley – SAS Institute Inc., Cary, NC, USA.
  • 14. Lamim Filho P. C. M., Pederiva R., Brito J. N. (2014), Detection of stator winding faults in induction machines using flux and vibration analysis, Mechanical Systems and Signal Processing, 42, 377-387.
  • 15. Matter U., Stutzer A. (2015), pvsR: an open source interface to big data on the American Political Sphere, PLOS ONE, 10, 7, e0130501, https://doi.org/10.1371/journal.pone.0130501.
  • 16. Nahmias S., Olsen T. L. (2015), Production and operations analysis: strategy, quality, analytics, application, Waveland Press, Long Grove, Illinois.
  • 17. Rai A., Upadhyay S. H. (2016), A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings, Tribology International, 96, 289-306.
  • 18. Regazzi A. J. (2000), Multivariate analysis, class notes INF 766, Department of Information Technology, Universidade Federal de Viçosa.
  • 19. Reis D. A. S. (2017), Discriminant Analysis and Optimization applied to the noise quality control of compressors using the softwares R and Gurobi, M.Sc. Dissertation, Universidade Federal de Uberlândia, Uberlândia.
  • 20. Reis E. (1997), Applied multivariate statistics, Edition Silabo, Lisboa.
  • 21. Sánchez R. S., Garrido J. C. F., Bolívar J. P. (2018), Noise monitoring networks as tools for smart city decision-making, Archives of Acoustics, 43, 1, 103-112.
  • 22. Santos C. G. P., Mato L. F., Clennell B. (2003), Discriminant Analysis applied to the characterization of the reservoir of Campo de Namorado Rio de Janeiro, Brasil [in Portugese]. Brazilian Congress of Research and Development in Oil and Gas.
  • 23. Sartorio S. D. (2008), Applications of multivariate analysis techniques in agricultural experiments using R software, M. Sc. Dissertation, Piracicaba – SP, Universidade de São Paulo – Escola Superior de Agricultura Luiz de Queiroz.
  • 24. Taha H. A. (2008), Operational research: an overview, Pearson Prentice Hall, São Paulo.
  • 25. TECUMSEH (2016), Tecumseh Products Company. Retrieved from http://www.tecumseh.com/pt/south-america.
  • 26. Vishwakarma M., Purohit R., Harshlata V., Rajput P. (2017), Vibration analysis & condition monitoring for rotating machines: a review, Materials Today: Proceedings, 4, 2659-2664.
  • 27. Wang H., Cheng G., Deng G., Li X., Li H., Huang Y. (2017), A fast method of feature extraction for lowering vehicle pass-by noise based on nonnegative Tucker3 decomposition, Archives of Acoustics, 42, 4, 619-629.
  • 28. Zuge M., Chaves Neto A. (1999), Use of multivariate statistical methods in the evaluation of corporate performance [in Portugese], Magazine Paranaense de Desenvolvimento, Curitiba, 97, 101-112.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-26b7a5c2-84c3-4c05-8027-6e4e5a603024
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