2026-04-07T14:30:45Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/18832026-03-17T08:01:20Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Serradilla, Oscar author Zugasti, Ekhi author Cernuda, Carlos author Zurutuza, Urko author 2020 This paper presents the implementation and explanations of a remaining life estimator model based on machine learning, applied to industrial data. Concretely, the model has been applied to a bushings testbed, where fatigue life tests are performed to find more suitable bushing characteristics. Different regressors have been compared Environmental and Operational Condition and setting variables as input data to prognosticate the remaining life on each observation during fatigue tests, where final model is a Random Forest was chosen given its accuracy and explainability potential. The model creation, optimisation and interpretation has been guided combining eXplainable Artificial Intelligence with domain knowledge. Precisely, ELI5 and LIME explainable techniques have been used to perform local and global explanations. These were used to understand the relevance of predictor variables in individual and overall remaining life estimations. The achieved results have been process knowledge gain and expert knowledge validation, assertion of huge potential of data-driven models in industrial processes and highlight the need of collaboration between expert knowledge technicians and eXplainable Artificial Intelligence techniques to understand advanced machine learning models. 978-1-7281-6932-3 1558-4739 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=161567 https://hdl.handle.net/20.500.11984/1883 Explainable Artificial Intelligence interpret Machine learning data-driven model Remaining Useful Life prognosis industrial process domain knowledge Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery