Industry Week - December 1, 2008
A Georgia Tech professor believes his machine models can more accurately predict the amount of remaining useful life of different mechanical devices, including electrical systems, than current sensor-based predictive methods. Nagi Gebraeel, an assistant professor in Georgia Tech's engineering school, has created stochastic models (measures of probability) to identify condition-based signals, which could be used to predict maintenance needs of critical components. Gebraeel says his tests can reduce total failure costs and expenses related to depletion of spare-parts inventory by 55%.
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