<![CDATA[The development of the Internet of Things (IoT) in predictive maintenance (IoT-enabled Predictive Maintenance) for industrial electrical equipment offers significant potential to enhance system efficiency and reliability; however, its implementation is constrained by challenges related to sensor data integration, communication infrastructure quality, and security issues. This study addresses a gap in the literature by describing patterns of successful IoT-enabled Predictive Maintenance implementation in industrial electrical applications. The contribution of this research lies in providing a systematic synthesis of leading technologies and key success factors in the adoption of IoT-enabled Predictive Maintenance. The method employed is a Systematic Literature Review (SLR) using the PRISMA approach, which resulted in 16 relevant articles. The findings indicate that the combination of IoT technologies, sensors, wireless networks, and edge-cloud architecture represents an appropriate technological configuration for building an effective Predictive Maintenance chain. These implementations are predominantly found in the manufacturing, energy, and transportation sectors, with the main success factors determined by data quality and network sustainability. These findings offer practical solutions for industry practitioners in improving the efficiency and sustainability of their systems. In conclusion, the successful implementation of IoT-enabled Predictive Maintenance in industrial electrical systems is highly dependent on the suitability of technological infrastructure, data governance, and service-based business models, while also opening opportunities for further research and the expansion of applications into other sectors.]]>
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