A New Methodological Framework for Optimizing Predictive Maintenance Using Machine Learning Combined with Product Quality Parameters

Abstract

Predictive maintenance (PdM) is the most suitable for production efficiency and cost reduction, aiming to perform maintenance actions when needed, avoiding unwanted failures and unnecessary preventive actions. The increasing use of 4.0 technologies in industries has allowed the adoption of recent advances in machine learning (ML) to develop an effective PdM strategy. Then again, production efficiency not only considers production volumes in terms of pieces or working hours, but also product quality (PQ), which is an important parameter to also detect possible defects in machines. In fact, PQ can be used as a parameter to predict possible failures and deeply affects manufacturing costs and reliability. In this context, this study aims to create a product performance-based maintenance framework through ML to determine the optimal PdM strategy based on the desired level of product quality and production performance. The framework is divided into three parts, starting from data collection, through the choice of the ML algorithm and model construction, and finally, the results analysis of the application to a real manufacturing process. The model has been tested within the production line of electromechanical components. The results show that the link between the variables representing the state of the machine and the qualitative parameters of the production process allows us to control maintenance actions based on scraps optimization, achieving an improvement in the reliability of the machine. Moreover, the application in the manufacturing process allows us to save about 50% of the costs for machine downtime and 64% of the costs for scraps.