@ARTICLE{Coral_Rodrigo_Development_2015,
 author={Coral, Rodrigo and Flesch, Carlos A. and Penz, Cesar A. and Borges, Maikon R.},
 volume={vol. 22},
 number={No 1},
 journal={Metrology and Measurement Systems},
 pages={79-88},
 howpublished={online},
 year={2015},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 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.},
 type={Artykuły / Articles},
 title={Development of a Committee of Artificial Neural Networks for the Performance Testing of Compressors for Thermal Machines in Very Reduced Times},
 URL={http://www.journals.pan.pl/Content/90302/PDF/Journal10178-VolumeXXII%20Issue1_07.pdf},
 doi={10.1515/mms-2015-0003},
 keywords={refrigeration compressor, artificial neural networks, performance test},
}