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.

Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance

“…Industry 4.0 gave rise to several concepts and one of them is PdM. PdM is one of the main pillars of Industry 4.0, and it is regarded as the digital type of machine maintenance [13]. PdM utilizes predictive tools such as artificial intelligence algorithms to determine when maintenance is truly necessary to be conducted [11].…”

“…Similarly, the author in [5] suggests periodic maintenance to prevent robotic failures and achieve higher reliability of robotic systems. In Reference [13], the authors highlight the importance of PdM since it offers the longest life and highest reliability of equipment. Next, the authors in [14] suggest a data-driven PdM approach for reliability assessment and improvement due to simplifications and assumptions taken into account during the modeling of the robotic cell, such as the selection of the exponential distribution as the reliability function and the constant failure rate.…”

Implementation of Reliability Centered Maintenance (RCM) in the background of industry 4.0 –Issues, Challenges, and Opportunities

“…In the context of Industry 4.0, PdM employs sophisticated online analysis of the data gathered for the early identi cation of probable equipment problems and assists engineers with maintenance operations by providing guided smart decision assistance. Cyber-physical systems (CPS), the Internet of Things (IoT), mobile devices, cloud computing, big data, and sophisticated AI approaches are among the key technologies of Industry 4.0 [19,20]. Cyber-physical systems and sensors are also being used for digitizing physical processes and integrating control operations and ITbased data-driven automation as part of Industry 4.0.…”

Spatial Data-Based Automatic and Quantitative Approach in Analyzing Maintenance Reachability

“…Specifically, qualitative design and analysis are conducted by maintainability check, and quantitative design and analysis are conducted by allocation and prediction. In recent years, advanced maintenance technology has been introduced, for instance, the intelligent predictive maintenance is more in line with the industrial development trend, such as condition-based maintenance, prognostics and health management, and remaining useful life estimation [15], and this maintenance policy has been adopted by many industries in various domains. In this advanced maintenance method, intelligent sensors are extremely essential for the realization of predictive maintenance [16][17][18].…”