Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

4 Issues per year


IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228

Open Access
Online
ISSN
2300-1941
See all formats and pricing
More options …
Volume 22, Issue 1 (Mar 2015)

Issues

Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times

Rodrigo Coral
  • Corresponding author
  • Dep. de Eletroeletrônica, Instituto Federal de Santa Catarina, 89220-200, Joinville, SC, Brazil
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Carlos A. Flesch
  • Dep. de Engenharia Mecânica, Universidade Federal de Santa Catarina, 88040-970, Florianópolis, SC, Brazil
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Cesar A. Penz
  • Dep. de Engenharia Mecânica, Universidade Federal de Santa Catarina, 88040-970, Florianópolis, SC, Brazil
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Maikon R. Borges
Published Online: 2015-02-20 | DOI: https://doi.org/10.1515/mms-2015-0003

Abstract

This paper presents a new test method able to infer - in periods of less than 7 seconds - the refrigeration capacity of a compressor used in thermal machines, which represents a time reduction of approximately 99.95% related to the standardized traditional methods. The method was developed aiming at its application on compressor manufacture lines and on 100% of the units produced. Artificial neural networks (ANNs) were used to establish a model able to infer the refrigeration capacity based on the data collected directly on the production line. The proposed method does not make use of refrigeration systems and also does not require using the compressor oil.

Keywords: refrigeration compressor; artificial neural networks; performance test

References

  • [1] ASHRAE STANDARD, (2005). ANSI/ASHRAE 23: Methods of testing for rating positive displacement refrigerant compressors and condensing units. USA.Google Scholar

  • [2] DIN - DEUTSCHES INSTITUT FÜR NORMUNG, (2008). EN 13771-1: Compressors and condensing units for refrigeration - Performance testing and test methods - Part 1: Refrigerant compressors. Germany.Google Scholar

  • [3] ISO - INTERNATIONAL ORGNIZATION FOR STANDARDIZATION., (1989). ISO 917 - Testing of refrigerant compressors, second ed., Switzerland.Google Scholar

  • [4] Penz, C. A., Flesch, C. A., Nassar, S. M., Flesch, R. C. C., Oliveira, M. A., (2012). Fuzzy-Bayesian network for refrigeration compressor performance prediction and test time reduction. Expert Syst. with Appl., 39, 4268-4273.Web of ScienceCrossrefGoogle Scholar

  • [5] Flesch, R. C. C., Normey-Rico, J. E., (2010). Modelling, identification, and control of a calorimeter used for performance evaluation of refrigerant compressors. Control Eng. Pract., 18, 254-261.Web of ScienceCrossrefGoogle Scholar

  • [6] Gustafson, S., Little, G. R., (1992). Correlation of transient and steady-state compressor performance using neural networks. In Proc. of the AutoTest Conf. 92, USA, 69-72.Google Scholar

  • [7] Stoecker, W. J., Jabardo, J. M. S., (2002). Industrial refrigeration, second ed., Edgard Blücher, São Paulo.Google Scholar

  • [8] Haykin, S., (1999). Neural Networks: a comprehensive foundation. NJ: Pearson Education, India.Google Scholar

  • [9] Singaram, L. A., (2011). Prediction models for mechanical properties of AZ61 MG alloy fabricated by equal channel angular pressing. Int. J. of Res. and Rev. in Appl. Sci., 8, 337-345.Google Scholar

  • [10] Ghobadian, B., Rahimi, H., Nikbakht, A. M., Najafi, G., Yusaf, T. F., (2009). Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network. Renew. Energy, 34, 976-982.CrossrefGoogle Scholar

  • [11] Ertunc, H. M., Hosoz, M., (2005). Artificial neural network analysis of a refrigeration system with an evaporative condenser. Appl. Therm. Eng., 26, 627-635.CrossrefGoogle Scholar

  • [12] Arcaklioğlu, E., Çavuşoğlu, A., & Erişen, A., (2004). Thermodynamic analysis of refrigerant mixtures using artificial neural networks. Appl. Energy, 78, 219-230.CrossrefGoogle Scholar

  • [13] Russel, S., Norvig, P., (2003). Artificial Intelligence: A Modern Approach, second ed. Prentice Hall, New York.Google Scholar

  • [14] Hu, Y. H., Hwang, J., (2002). Handbook of neural network signal processing. CRC Press, New York.Google Scholar

  • [15] Granitto, P. M., Verdes, P. F., Ceccatto, H. A., (2005). Neural Networks Ensembles: Evaluation of Aggregation algorithms. Artif. Intelligence, 163, 139-162.CrossrefGoogle Scholar

  • [16] Zio, E., (2006). A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes. IEEE Trans. on Nucl. Sci., 53, 1460-1478.CrossrefGoogle Scholar

  • [17] Trichakis, I., Nikolos, I., Karatzas, G. P., (2011). Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response. Hydrol. Processes, 25, 2827-2836.Web of ScienceGoogle Scholar

  • [18] Papadopoulos, G., Edwards, P. J., Murray, A. F., (2000). Confidence estimation methods for neural networks: a practical comparison. In Proc. of the Eur. Symp. on Artif. Neural Netw., Bruges, Belgium, 75-80.Google Scholar

  • [19] BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, OIML, (2008). JCGM 100: Evaluation of measurement data - Guide to the expression of uncertainty in measurement, France.Google Scholar

  • [20] Efron, B., Tibshirani, R., (1993). An introduction to the bootstrap. Chapman & Hall, New York.Google Scholar

  • [21] Sharkey, A. J. C., (1999). Combining artificial neural networks: ensemble and modular multi-net systems. Springer-Verlag, London.Google Scholar

  • [22] Zhang, J., (1999). Developing robust non-linear models through bootstrap aggregated neural networks. Neurocomputing, 25, 93-113.CrossrefGoogle Scholar

  • [23] Yu, J. B., Xi, L. F. A., (2009). Neural network ensemble-based model for online monitoring and diagnosis of out-of-control signals in multivariate manufacturing processes. Expert Syst. with Appl., 36, 909-921.CrossrefWeb of ScienceGoogle Scholar

  • [24] Wu, B., Yu, J., (2010). A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes. Expert Syst. with Appl., 37, 4058-4065.Web of ScienceCrossrefGoogle Scholar

  • [25] Gayeski, N.T., Zakula, T., Armstrong, P.R., (2010). Empirical modeling of a rolling-piston compressor heat pump for predictive control in low lift cooling. ASHRAE Trans., 116.Google Scholar

  • [26] Swider, D. J., Browne, P. K., Bansal, V., (2001). Modelling of vapour-compression liquid chillers with neural networks. Appl. Therm. Eng., 21, 311-329.CrossrefGoogle Scholar

  • [27] Kim, T., Li, C. J., (1995). Feedforward neural networks for fault diagnosis and severity assessment of a screw compressor. Mech. Syst. and Signal Processing, 9, 485-496. Google Scholar

About the article

Received: 2014-05-06

Accepted: 2014-09-27

Published Online: 2015-02-20

Published in Print: 2015-03-01


Citation Information: Metrology and Measurement Systems, ISSN (Online) 2300-1941, DOI: https://doi.org/10.1515/mms-2015-0003.

Export Citation

© Polish Academy of Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Eric Aislan Antonelo, Carlos Alberto Flesch, and Filipe Schmitz
Neurocomputing, 2017

Comments (0)

Please log in or register to comment.
Log in