Trends in Elevator Maintenance Strategies: An In-Depth Investigation

This extensive investigation aims to assess prevailing trends in the methodologies employed for elevator maintenance, alongside their implications for safety, reliability, and economic feasibility. This systematic review comprehensively analyzed a total of 87 peer-reviewed articles diligently retrieved from five separate databases (specifically, IEEE, ScienceDirect, Google Scholar, Emerald Insight, and Springer) published within the temporal frame of 2018 to 2024, inclusive. The results suggest a significant transition towards data-driven maintenance frameworks, particularly emphasizing condition-based and predictive maintenance strategies. It pertains to the utilization of IoT technologies to enhance operational efficiency and minimize expenditure. The application of IoT in maintenance has demonstrated its capacity to prevent downtime and bolster system reliability. The investigation further clarifies the persistent importance of amalgamating Condition-based and Time-based maintenance recovery strategies to optimize the benefits realized. These findings underscore the need for the development of comprehensive and adaptable maintenance policies that can address the diverse challenges encountered in the domain of elevator maintenance. Subsequent research initiatives should focus on measuring the enduring benefits that these novel strategies entail, particularly in terms of improvements in both safety and economic viability. Furthermore, the manuscript emphasizes the need for empirical research into the integration of Risk-based maintenance with existing methodologies, as well as the development of a comprehensive best practice manual for implementing Internet of Things (IoT) technologies in elevator maintenance, thereby enhancing this aspect of sustainable building upkeep.