# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
###### 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. 1 2022 2022  
(CPS) play an important role in this context by introducing cognitive automation and, consequently, implementing the concept of intelligent production, leading to intelligent products and services. This innovative approach leads companies to meet the challenges of a much more dynamic environment. In this regard, companies are able to maximize the life of their equipment while avoiding unplanned downtime and minimizing energy consumption and costs through the application of predictive maintenance [3]. Predictive maintenance has become a promising approach, providing solutions for the remaining life of equipment through the prediction of data collected by various sensors on equipment [4]. It has reached critical importance for industries due to the increasing complexity of interactions between different production activities in increasingly large manufacturing ecosystems. Figure 1 shows the global trend towards the application of this type of smart maintenance with a prediction of 2022 to 2030. In general, the contemporary maintenance approaches vary depending on the different learning models used and the different problems encountered by the machines/equipment. However, the fault diagnosis and prognosis step in predictive maintenance must be very precise and clear [5]. Given the goal of building an intelligent manufacturing system, the detection, interpretation and prediction of failures that occur in equipment are based on data acquired from sensors on the equipment [4]. However, adopting these technological advances does not come alone. It is important to have a good idea of the benefits and challenges of applying Maintenance 4.0 whether it is on the financial, organizational, data source, or equipment repair side. Predictive maintenance indeed consists of data mining for the formulation of machine learning models that manage to gain knowledge and predict the state of health of equipment. In this regard, predictive maintenance offers methods such as Condition-Based Maintenance (CBM), Prognostics and Health Management (PHM), and Remaining Useful Life (RUL). This article aims at presenting an exhaustive review of methods and tools used in smart maintenance to offer an academic and technical reference presenting challenges and opportunities in this field.