Development of a Production Machine Maintenance Predictive Model Using the Elman Recurrent Neural Network Algorithm

Abstract

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>PT Simba Indosnack Makmur is a factory that produces snacks. In the production process the machine has worked very optimally, the problem that is often faced by the Quality Control department is often finding non-standard product weights. This problem is caused by a machine that already requires maintenance. So far, the maintenance process has to get approval from the manager, which sometimes takes quite a long time to be inspected so that the maintenance process is delayed, which results in reduced production targets. By implementing a predictive maintenance model that utilizes time series data in the production process, applying the Elman Recurrent Neural Network will be able to provide notifications for machine maintenance before the machine is inaccurate in snack production. The Elman structure was chosen because it can make iterations much faster, thus facilitating the convergence process. The input vector used uses windows size. The results of the study using a target error of 0.001 show the smallest MSE value of 0.002833 with windows size 11. Then by using 13 neurons in the hidden layer a minimum error value of 0.003725 is obtained.</p></td></tr></tbody></table>