Temperature-Effect Compensation for Leak Detectors by Using Machine Learning Techniques

Autor:	Andoni Bilbao, Xabier Oregui Biain, Zelmar Etxegoien, Juan Luis Ferrando Chacón, Ander García Gangoiti, Eneko Fernandez
Rok vydání:	2021
Předmět:	Leak business.industry Detector Leak detector Process (computing) Machine learning computer.software_genre Automation Compensation (engineering) Multilayer perceptron Environmental science Artificial intelligence Stage (hydrology) business computer
Zdroj:	16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) ISBN: 9783030878689 SOCO
DOI:	10.1007/978-3-030-87869-6_51
Popis:	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%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::3ab1d23bd990310aa487dfc9fa811e0a https://doi.org/10.1007/978-3-030-87869-6_51 Zobrazit plný text záznamu