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A Neural Network based Approach to Predict Machine Status for Big Data using R


Author : Swapnil Khobragade and Ganesh. K. Pakle

Pages : 2383-2389
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Abstract

Big data studies recognize some of the major issues growing up in current market, like engaging new customers is quite difficult and not affordable than having legacy of old customers. Machine status (e.g. ON/OFF) prediction model to effectively manage the situation and to maintain efficient work flow in order to control various machinery are developed by academics and practitioners in past few years. Identifying machine status of operating machines is an important activity for mechanical industries, the caliber to properly predict machine status is exigent. As the automation in the mechanical industries era gets more emulative since 20s, machine status prediction management is a crucial task for machine industries where number of large scale machines operates. The article tenders a neural network founded outlook for prediction of machine status in association with various log records. Neural network associated nearness to the results of experiments more than 95% accuracy is being calculated with a machine data that can predict conditions. Further, it is in observation that medial sized Neural Networks performing excellent operations over the model designed for machine status to predict when various neural network’s strategies taken & examined.

Keywords: Neural Network, Machine status prediction, Predictive analytic, Machine management

Article published in International Journal of Current Engineering and Technology, Vol.5, No.4 (Aug-2015)

 

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