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Variable Importance Assessment in Lifespan Models of Insulation Materials: A Comparative Study

Abstract : This paper presents and compares different methods for evaluating the relative importance of variables involved in insulation lifespan models. Parametric and non-parametric models are derived from accelerated aging tests on twisted pairs covered with an insulating varnish under different stress constraints (voltage, frequency and temperature). Parametric models establish a simple stress-lifespan relationship and the variable importance can be evaluated from the estimated parameters. As an alternative approach, non-parametric models explain the stress-lifespan relationship by means of regression trees or random forests (RF) for instance. Regression trees naturally provide a hierarchy between the variables. However, they suffer from a high dependency with respect to the training set. We show that RF provide a more robust model while allowing a quantitative variable importance assessment. Comparisons of the different models are performed on different training and test sets obtained through experiments.
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Submitted on : Monday, October 3, 2016 - 10:55:42 AM
Last modification on : Wednesday, October 14, 2020 - 3:41:59 AM
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Farah Salameh, Antoine Picot, Marie Chabert, Eve Leconte, Anne Ruiz-Gazen, et al.. Variable Importance Assessment in Lifespan Models of Insulation Materials: A Comparative Study. 10th IEEE International Symposium on Diagnostics, Power Electronics and Drives (SDEMPED 2015), Sep 2015, Guarda, Portugal. pp.198-204, ⟨10.1109/DEMPED.2015.7303690⟩. ⟨hal-01375415⟩



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