Proactive Learning for Intelligent Maintenance in Industry 4.0

Abstract

Manufacturing companies require efficient maintenance practices in order to improve business performance, ensure equipment availability and reduce process downtime. With the advent of new technology, manufacturing processes are evolving from the traditional ways into digitalized manufacturing. This transformation enables systems and machines to be connected in complex networks as a collaborative community through the industrial internet of things (IIoT) and cyber-physical system (CPS). Hence, advanced maintenance strategies should be developed in order to ensure the successful implementation of Industry 4.0, which aims to transform traditional product-oriented systems into product-service systems (PSS). Today, machines and systems are expected to gain self-awareness and self-predictiveness in order to provide management with more insight on the status of the factory. In this regards, real-time monitoring along with the application of advanced machine learning algorithms based on historical data will enable systems to understand the current operating conditions, predict the remaining useful life and detect anomalies in the process. This paper discusses the necessity of predictive maintenance to achieve a sustainable and service-oriented manufacturing system and provides a methodology to be followed for implementing proactive maintenance in the context of Industry 4.0.