Abstract
Although Differential Pressure Decay Testing (DPDT) is less influenced by external environment than pressure decay testing, the different temperatures involved in the process still affect the leak measurements, particularly in quick changing conditions. This paper investigates the impact of air, injected air and part temperature on leak measurements and develops a compensation model based on Machine Learning (ML) algorithms that uses these temperatures as predictors, as well as other such as maximum and minimum pressure during stabilization stage. An automated machine for data capture has been developed to simulate varying conditions. The results show that under the conditions investigated, the part temperature has the greatest impact on leak measurements. For the regressive model used in the compensation model, several ML algorithms are investigated, and the best results are obtained by using multilayer perceptron, reducing the mean absolute error measured by a commercial leak detector by 91%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lafferty, J.M., Rubin, L.G.: Foundations of vacuum. Phys. Today 52, 86–87 (1999)
Cincinnati Test Systems [Online]. https://www.cincinnati-test.com/pressure-decay-test/differential-pressure-decay
Calcatelli, A., Bergoglio, M., Mari,D.: Leak detection, calibrations and reference flows: practical example, Vacuum 81(11–12), 1538–1544 (2007)
Juliano, T.M., MeegodaJay, J.N., Meegoda, N., Watts,D.J.: Acoustic emission leak detection on a metal pipeline buried in sandy soil. J. Pipeline Syst. Eng. Pract. 4, 149–155 (2013)
Biram, J., Burrows, G.: Bubble tests for gas tightness. Vacuum 14, 221–226 (1964)
Kagawa, T., et al.: Heat conduction effects of the chamber wall on the air pressure dynamics. Trans. Soc. Instrum. Control Eng. 23 (1987)
Harus, L.G.: Characteristics of leak detection based on differential pressure measurement. In: Proceedings of the 6th JFPS International Symposium on Fluid Power, Tsukuba (2005)
Shi, Y., Tong, X., Cai, M.: Temperature effect compensation for fast differential pressure decay testing. Measure. Sci. Technol. 25 (2014)
Oo, L.L., Youn, C., Kagawa, T.: Temperature compensation in differential pressure method for air leakage detection. In: Asia Simulation Conference 2009, Shiga (2009)
Garcia, A., Ferrando, J.L., Arbelaiz, A., Oregi, X., Etxegoien, Z., Bilbao, A.: Soft computing analysis of pressure decay leak test detection. In: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, Burgos (Spain) (2020)
Drucker, H., Burges, C.C., Kaufman, L., Smola, A.J., Vapnik, V.N.: Support vector regression machines. Advances in Neural Information Processing Systems, pp. 155–161 (1997)
FionnMurtagh: Multilayer perceptrons for classification and regression. Neurocomputing 2(5–6), 183–197 (1991)
Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition (1995)
Rokach, L., Maimon, O.: Data mining with Decision Trees: Theory and Applications. World Scientific (2008)
Ostertagova, E.: Modelling using polynomial regression. Proc. Eng. 48, 500–506 (2012)
Yin, S., Zhu, X., Jing, C.: Fault detection based on a robust one class support vector machine. Neurocomputing 145, 263–268 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chacón, J.L.F., Gangoiti, A.G., Biain, X.O., Bilbao, A., Fernandez, E., Etxegoien, Z. (2022). Temperature-Effect Compensation for Leak Detectors by Using Machine Learning Techniques. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-87869-6_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87868-9
Online ISBN: 978-3-030-87869-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)