Variable importance assessment in lifespan models of insulation materials: A comparative study

• Published in: 2015 IEEE 10th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) pp. 198 - 204
• Main Authors: Salameh, F., Picot, A., Chabert, M., Leconte, E., Ruiz-Gazen, A., Maussion, P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2015
• Subjects: Computational modeling; design of experiments; Insulation; lifespan; modeling; outliers; Radio frequency; random forest; regression tree; Regression tree analysis; response surface; Stress; Temperature; twisted pairs; variable importance; Vegetation
• DOI: 10.1109/DEMPED.2015.7303690

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. This paper shows 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.