A Java application for the failure rate prediction using feed forward neural networks
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In this paper we show the possibility to use feed forward neural networks for failure rate prediction, and this can be used for improving predictive maintenance. We use a series of real values that represent the failure rate of a radio-reception system, and describe a Java application that we developed, that simulates a feed forward neural network which is trained to predict, based on the available series of failure rate values, the next failure rate value. We use a one hidden layer neural network that has a single output, the predicted value. The inputs of network are analog, continuous values between 0 and 1, and represent the previous known values of failure rate. Using the software, we used different numbers of input (2, 3, and 5) and the accuracy of prediction is compared for these different numbers.
[1] Andreas S. Weigend,et al. Time Series Prediction: Forecasting the Future and Understanding the Past , 1994 .
[2] Constantin Anton,et al. Improved by prediction of the PFMEA using the artificial neural networks in the electrical industry , 2011, 2011 International Conference on Applied Electronics.
[3] A. Blanc,et al. Total Productive Maintenance , 1993, International Symposium on Semiconductor Manufacturing.
[4] Adam Blum,et al. Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems , 1992 .