Failure Forecasting for Cost Effective Planned Replacement of Boeing 737 Brakes Employing Neural Network (original) (raw)

Abstract

The failure rate analysis of brake assemblies of a commercial airplane, i.e., Boeing 737, is analyzed using the artificial neural network and Weibull regression models. One-layered feed-forward back-propagation algorithm for artificial neural network whereas three parameters model for Weibull are used for the analysis. Three years of data are used for model building and validation. The results show that the failure rate predicted by neural network is closer in agreement with the actual data than the failure rate predicted by the Weibull model. Results also indicate that neural network can be effectively integrated into aviation cost effective maintenance facility computerized material requirement planning system to forecast the number of brake assemblies needed for a given planning horizon.

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