Leveraging industry 4.0 techniques for predictive equipment maintenance: From concept to commissioning

Abstract

In recent decades, lean manufacturing has significantly impacted the manufacturing industry, gaining widespread adoption. Companies have increasingly recognised the competitive advantages of implementing flow-oriented production layouts, demand-flow technologies, and just-in-time production. This shift has also transformed the approach to maintenance, moving away from reactive strategies. With production cells becoming more susceptible to system disturbances, maintenance managers are now focused on strategic maintenance development to ensure reliable production equipment. The emergence of Industry 4.0 technologies has further reshaped plant maintenance, providing companies with accurate and dependable tools for proactive maintenance. This paper proposes a hypothesis of research conducted at the University of Malta, focusing on the potential application of predictive maintenance (PdM) in the upkeep of automation machinery. It suggests the use of a novel data management and acquisition system, grounded in simulation and deep learning modelling, to predict the remaining useful life (RUL) of machinery early during the design machine realisation. This exploration lays the groundwork for potential development of a comprehensive maintenance tool. Such a tool would optimise automation designs, estimate maintenance costs throughout the machine’s lifecycle, and prevent machine breakdown through proactive interventions.