Enhanced Digital Twin for Human-Centric and Integrated Lighting Asset Management in Public Libraries: From Corrective to Predictive Maintenance

TL;DR

This paper presents an enhanced digital twin model for public library lighting assets that enables proactive, predictive maintenance, improving sustainability and human-centric lighting management through integrated analytics.

Contribution

The study introduces a comprehensive digital twin framework that combines descriptive, diagnostic, predictive, and prescriptive analytics for proactive lighting asset management in public libraries.

Findings

Supports early detection of lighting issues

Enables transition from reactive to predictive maintenance

Framework adaptable to other building assets

Abstract

Lighting asset management in public libraries has traditionally been reactive, focusing on corrective maintenance, addressing issues only when failures occur. Although standards now encourage preventive measures, such as incorporating a maintenance factor, the broader goal of human centric, sustainable lighting systems requires a shift toward predictive maintenance strategies. This study introduces an enhanced digital twin model designed for the proactive management of lighting assets in public libraries. By integrating descriptive, diagnostic, predictive, and prescriptive analytics, the model enables a comprehensive, multilevel view of asset health. The proposed framework supports both preventive and predictive maintenance strategies, allowing for early detection of issues and the timely resolution of potential failures. In addition to the specific application for lighting systems, the design is adaptable for other building assets, providing a scalable solution for integrated asset management in various public spaces.