Machine Life Prognosticator

Barbara Christopher
Industrial and Systems Engineering
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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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  • Created By: Barbara Christopher
  • Workflow Status: Published
  • Created On: Nov 30, 2008 - 8:00pm
  • Last Updated: Oct 7, 2016 - 11:06pm