Improvement of a pressure rise test rig for cooling capacity inference of hermetic compressors based on ANNs.

Recent publications have proposed the use of tools based on artificial neural networks to infer the cooling capacity of refrigeration compressors from the results of pressure rise tests, which are quick tests used for production quality assurance. However, the typical rigs used in such tests were not designed to evaluate compressor performance, so the uncertainty in the inferred cooling capacity is high. This paper proposes an improved test rig aiming a better correlation of its results with cooling capacity. A committee of multilayer perceptron artificial neural networks was used to make the cooling capacity inferences from the results obtained in the improved test rig. A method that combines bootstrap techniques with Monte Carlo simulations was used to assure the reliability of the results. The average absolute difference observed between the results of the proposed method and the results of traditional tests done in laboratory was 0.35%, with standard deviation of 0.47%. In addition, the average uncertainty of the inferences was 4.3% for the test samples, which is close to the uncertainty of 3.0% observed in traditional tests, both for a coverage probability of 95%. The time required to carry out the proposed test is about 1 min, thus enabling an increase in the sampling of tested compressors with respect to the traditional method used in industry. [ABSTRACT FROM AUTHOR]