Smartfix: Leveraging machine learning for proactive equipment maintenance in industry 4.0

Abstract

In the fast-moving field of Industry 4.0, active equipment maintenance is becoming increasingly critical. This article introduces an innovative solution called SmartFix that leverages machine learning to transform traditional maintenance methods. By analyzing data from the device's sensors in real time, SmartFix can predict potential failures in advance, enabling timely intervention. This proactive approach not only improves the stability and longevity of manufacturing equipment, but also significantly reduces unplanned downtime, optimising overall production efficiency. The integration of SmartFix within the Industry 4.0 framework marks an important shift towards smarter, data-driven manufacturing processes. 1. Introduction The advent of Industry 4.0 has revolutionized manufacturing, bringing advances in areas such as Internet of Things technology, robotics and big data analytics. At the heart of these changes is the evolution of equipment maintenance strategies from passive to proactive. Traditional maintenance strategies are often characterized by their reactivity, and have been lacking adequate countermeasures in the face of the complexity and needs of modern manufacturing industry. To combat this problem, this paper proposes an innovative approach, SmartFix, which utilizes machine learning techniques and aims to proactively identify and resolve device issues before they escalate significantly.