Predictive Maintenance in Smart Manufacturing: An IoT-Integrated Framework

Abstract

The very swift evolution of Industry 4.0 technologies has transformed conventional manufacturing into data-centric, networked manufacturing systems where efficiency, reliability, as well as sustainability are prime considerations. Against this setting, predictive maintenance (PdM) has gained considerable momentum as a significant method for reduction in equipment breakdown as well as resource utilization optimization. The current paper discusses the application of Internet of Things (IoT)-empowered predictive maintenance in smart manufacturing systems, while indicating its possibilities for migration from reactive as well as preventive strategies to data-centric, proactive strategies. The research posits a conceptual strategy that utilizes IoT-enabled sensing, real-time communications, data analytics, as well as statistical models to assemble forecasts of equipment breakdowns prior to their commencement. Strategic benefits that encompass cost optimization, increased product output, as well as sustainability enhancement are discussed in concurrence with challenges in the form of interoperability, cybersecurity risks, organizational reluctance, as well as the lack in standardized frameworks. The discussion also involves predictive maintenance evolution towards prescriptive ones, integration in digital twins, as well as the human-centric Industry 5.0 approach. Considering that empirical adoption remains in budding stages, this research identifies the necessity for scalable architectures as well as future studies that converge technical innovations as well as commercial viability. Overall, this study places IoT-enabled predictive maintenance as the foundation upon which resilience, sustainability, as well as competitiveness in next-generation smart manufacturing systems can obtain.