Predicting machine malfunctions in industrial facilities using artificial neural networks (a case study of the General Tobacco Corporation)
Keywords:
Artificail Neural Network, Matlab Program, Maintenance operations, Maintenance Management, feed-forward network, Artificial intelligence.Abstract
The research amis to predict the failures of industrial facilities to reduce the time period to complete the maintenance process and thus put the machines into service during the shortest possible period of time for the continuation of the production process though the use of neutral network models with their computational mathematical structure that has the ability to determine the complex relationship between input and output data. Where the matlab program was relied upon to bulid, simulate and train neural networks due to its high efficiency in the field of prediction using a feed-forward network according to the Backpropagation algorithm by determining the amount of output for the machine number6 LOGA2-LOF in the morning,evening and night shifts as input rays, and training the neural network based on the values of weights and primary biases changing the values to reach the best values enables us to reach an actual output, and the results proved the success of neural networks in predicting machine failures at an early stage.
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