Prediction of vacuum cleaner motor brush life: a regression approach

Abstract

The main focus of this paper is the empirical modelling of the wear of carbon brushes. Rather than determining the dominant wear mechanisms, an approach towards the prediction of wear under a range of different conditions was used. The models were obtained by multiple regression analysis using lifetime (LT) data contributed by the biggest European manufacturer of vacuum cleaner motors. This included reliability data for 607 different test populations involving 3980 motors. Exploration of the data revealed that wear-out parameters behaved in accordance with the existing field theory, giving additional confidence to the models. The numerical appreciation of the wear-out parameters and the resulting conclusions will be beneficial to motor design and reliability engineers. Learned knowledge will be used for faster selection of optimal design and operational motor parameters to meet recent EU regulation 666/2013. Along with the more rapid design of the product, a reduced number of LT tests will result in significant energy savings.