Data Mining to Enhance Reliability of Industrial Systems

Abstract

This project aims to enhance the reliability and optimize the performance of industrial systems through data mining and predictive maintenance. By analyzing and understanding the sensor data and then utilizing regression algorithms, a KNN regressor model is trained to predict future values. These forecasts are subsequently integrated into the predictive maintenance strategy to facilitate the prevention of failures, reduce downtime, and improve operational efficiency. This project will showcase the efficacy of this approach through a comprehensive case study conducted at IRIS-Tyres for the Quintoplex machine.