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•Failure modes and life prediction model for high-speed bearings in through-flow motor.•The forecast accuracy of the model is 70.82%.•The most influential life parameters and their ...magnitude have been identified.•Machine learning workflow applied to bearing life forecasting.•The database of 170 test populations is available as supplementary material.
The focus of this study was the empirical modelling of the high-speed bearings life and failure modes of a through-flow universal motor. An approach was used facilitating predictions of the life through-range of various conditions. The model estimates bearing life for the survival probability of 50% – L50. It influences parameters such as bearing temperature, speed factor, equivalent load, grease fill amount, type of oil, type of bearing cage, type of seals, tolerance class, and side of the motor, all of which are considered in the model. Initial empirical data consisted of 4672 test populations, involving 38,021 vacuum cleaner motors. Strict filtering requirements of all the available test data resulted in 170 final populations, consisting of 1385 tested and 638 failed bearings, which were used for building a Weibull database and for developing the models. The paper’s key contributions are the empirical models gained with multiple linear regression and the obtained database of tested bearings.
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.
Being able to predict temperature rise inside a machine is as important as predicting its performance and life. Because temperature measurements and computational thermal simulations can be time ...consuming, thermal paths inside the through-flow universal motor were described by means of simple lumped parameter thermal network. Once the model was built, its unknown convection coefficients were tuned with the genetic algorithm tool in MatLab. The model has been applied and successfully verified with measurements on two different types of a vacuum cleaner motor. Taking account of impeller losses as one of the model inputs makes temperature estimates more accurate regardless of machine’s operational regime.
Biti u stanju predvidjeti porast temperature unutar stroja jednako je važno kao i predvidjeti njegovo djelovanje i radni vijek. Budući da mjerenja temperature i toplinske simulacije računalom mogu ...zahtijevati puno vremena, putovi topline unutar protočnog univerzalnog motora su opisani jednostavnom toplinskom mrežom skupnog (lumped) parametra. Jednom kad je model izgrađen, njegovi nepoznati koeficijenti konvekcije su usklađeni s alatom genetičkog algoritma u MatLab. Model je primijenjen i uspješno provjeren mjerenjima na dva različita tipa motora usisivača za prašinu. Uzimajući u obzir gibitke rotora kao jednog od ulaza modela, procjene temperature su točnije bez obzira na radni režim stroja.
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