On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges

TLDR

Predictive maintenance, a key component of Industry 4.0, leverages the exponential growth of sensor data to reduce downtime, extend machine life, and improve production quality through a precise workflow from data collection to decision‑making. This paper reviews and categorizes the life cycle, challenges, and models—condition‑based maintenance, prognostics and health management, and remaining useful life—of intelligent predictive maintenance, and proposes a novel industrial workflow with a decision‑support recommendation. The proposed platform facilitates seamless data communication across equipment throughout the maintenance life cycle and supports decision‑making in smart maintenance.

Abstract

In the era of the fourth industrial revolution, several concepts have arisen in parallel with this new revolution, such as predictive maintenance, which today plays a key role in sustainable manufacturing and production systems by introducing a digital version of machine maintenance. The data extracted from production processes have increased exponentially due to the proliferation of sensing technologies. Even if Maintenance 4.0 faces organizational, financial, or even data source and machine repair challenges, it remains a strong point for the companies that use it. Indeed, it allows for minimizing machine downtime and associated costs, maximizing the life cycle of the machine, and improving the quality and cadence of production. This approach is generally characterized by a very precise workflow, starting with project understanding and data collection and ending with the decision-making phase. This paper presents an exhaustive literature review of methods and applied tools for intelligent predictive maintenance models in Industry 4.0 by identifying and categorizing the life cycle of maintenance projects and the challenges encountered, and presents the models associated with this type of maintenance: condition-based maintenance (CBM), prognostics and health management (PHM), and remaining useful life (RUL). Finally, a novel applied industrial workflow of predictive maintenance is presented including the decision support phase wherein a recommendation for a predictive maintenance platform is presented. This platform ensures the management and fluid data communication between equipment throughout their life cycle in the context of smart maintenance.