Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/41323
Title: Variables influence analysis of gas leak testing using belief propagation over factor graphs
Author: Martins, Joana
Costa, Diogo
Rocha, Eugénio
Keywords: Belief propagation
Factor graphs
Gas leak testing
Industry 4.0
Issue Date: 2023
Publisher: Elsevier
Abstract: Leak testing provides nondestructive quality measurements and is a vital stage in the manufacturing of components that require leak-tight properties. Yet, leak tests are notorious for their sensitivity towards external and environmental factors, hampering accuracy and reliability of test results, leading to a more costly and less efficient production cycle. The classical approach to this issue is through the use of mathematical or physical models of leakage behaviour, which are of difficult creation and low generalisation. Yet, resulting calibrations made to compensate for small deviations in testing conditions seldom consider each test-jig's unique set-up, and may still lead to unsatisfactory results. Alternatively, data-driven methods, such as the novel approach presented in this work, allow for failure analysis based solely on historical data. To achieve that, we employ a set of Variable Influence Analysis (VIA) models based on the application of the Belief Propagation algorithm to factor graphs where different events are related through correlation relationships. What differentiates our approach from other data-driven methods, such as the ones based on machine learning or deep learning methods, is the interpretability of the results and more efficient and swift implementation. We then apply VIA models to a real-world use case at Bosch Thermotechnology, centred around a differential pressure leak tester where testing frequently resulted in false rejections. Our approach is able to formally determine in a data-driven manner that, unlike initial suspicion, environmental factors show negligible impact on false rejections, and issues likely stem from equipment fault.
Peer review: yes
URI: http://hdl.handle.net/10773/41323
DOI: 10.1016/j.procs.2022.12.311
ISSN: 1877-0509
Appears in Collections:CIDMA - Artigos
DFis - Artigos
DMat - Artigos
DEM - Artigos
FAAG - Artigos

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