Predictive Maintenance and Engineered Processes in Mechatronic Industry: An Italian Case Study

Abstract

The paper proposes the results of a research industry project concerning predictive maintenance process optimization, applied to a machine cutting polyurethane. A company producing cutting machines, has been provided with an online control system able to detect blade status of a machine supplied to a customer producing polyurethane components. A software platform has been developed for the real time monitoring of the blade status and for the prediction of the break up conditions adopting a multi-parametric data analysis approach, based on the simultaneous use of unsupervised and supervised machine learning algorithms. Specifically, the proposed method adopts a k-Means algorithm to classify bidimensional risk maps, and a Long Short Term Memory (LSTM) one to predict the alerting levels based on the analysis of the last values for some process variables. The analysed algorithms are applied to an experimental dataset.