Implementation of Artificial Intelligence for Predicting Non-Quality Failures

Abstract

The prediction of failures in a factory is now an important area of industry that helps to reduce time and cost of non-quality from the data generated from the sensors installed on production lines, this data is used to detect anomalies and predict defects before they occur. The purpose of this article is to model an intelligent system that predicts different types of machine failures using the artificial neural network method. First, we started with a description of the approach to detecting and improving a non-quality rate. Secondly, we detailed the neural network method and its development disciplines. After that, we proposed the new failure prediction model using differents hyperparameters of neural networks, this model is applied into a cardboard compactor as a case study in this article. Finally, we demonstrate the accuracy and performance of the proposed model based on the results obtained.