Jul 2026· Journal of Facade Design and Engineering· Vol 14, pp. 17-38· 0 citations· 22 references
TL;DR
The design phase of an Extended Reality (XR)-based platform aimed at optimising operation and maintenance for dynamic building envelopes is presented, which uses cloud architecture and consumer smart devices to deliver scalable, cost-effective maintenance workflows with XR technology.
Abstract
This paper presents the design phase of an Extended Reality (XR)-based platform aimed at optimising operation and maintenance for dynamic building envelopes. The proposed solution addresses the growing need for advanced facility management tools that support energy performance and operational efficiency throughout the building lifecycle. The platform integrates heterogeneous data sources, including Building Information Modelling (BIM), Internet of Things (IoT) sensors, and Digital Product Passports (DPPs), to enable real-time fault detection, remote technical assistance, and enhanced communication between key actors. Built on user story methodologies, the platform uses cloud architecture and consumer smart devices to deliver scalable, cost-effective maintenance workflows with XR technology. In addition, Artificial Intelligence (AI) capabilities are integrated to support historical data analysis and automated reporting generation. Preliminary platform validations in real-world environments have demonstrated encouraging results in terms of increased building intelligence, maintenance operation efficiency, and user satisfaction.
Building maintenance is essential for sustaining operational efficiency and asset longevity, yet many organizations still rely on manual, fragmented processes. This study presents an integrated methodology that combines Building Information Modelling (BIM) with a custom desktop application to automate maintenance scheduling and support structured knowledge management. The approach follows a continuous data loop comprising BIM, Dynamo, Excel, and an application that enables seamless data exchange, real-time updates, and full traceability. The methodology was validated through a case study of a Bank facility in Egypt, focusing on door assets due to their critical security and operational roles. BIM data was extracted, enriched with manufacturer maintenance instructions, and processed within the custom application, which generated maintenance schedules. The system prioritized tasks based on service life, budget constraints, and asset criticality, while also capturing technician feedback to refine future planning. Implementation results demonstrated a 75% reduction in manual scheduling time, an increase in on-time task execution from 65% to over 92%, and complete coverage of maintenance data. Additionally, the solution improved traceability, transparency in budgeting, and stakeholder confidence. This research illustrates the potential of BIM-integrated, automation-ready maintenance systems to improve decision-making, reduce operational costs, and extend asset life cycles, particularly in high-security, high-access environments.
Ahmed E. Mansour, M. Elbehery, Sherief A. Ibrahim et al.· IOP Conference Series: Earth...· 0 citations
This framework proves an autonomous decision-making system that organically links inspection data with maintenance regulations by transforming static, manual-labor-centered maintenance workflows into intelligent automated models and increases the efficiency of railway infrastructure management while providing a scalable technical foundation for overall asset management of future smart-city infrastructure.
Minjae Jeon, Yong-Gyun Kim, Seok-Han Kim· Smart Cities· 0 citations
Railway maintenance relies on effective assessment of asset health to ensure safety and reliability. It requires appropriate technologies, methodologies, and tools for monitoring the condition of railway assets. These technologies and tools often utilize advanced sensor technology for data acquisition, distributed computing for data processing, enhanced analytics for knowledge extraction, and visualization technologies for data representation. While significant progress has been made in data acquisition, processing, and analytics, data visualization has not received sufficient attention. In parallel, the emerging Industry 5.0 concept emphasizes human-centricity, resilience, and sustainability in industrial systems, highlighting the need for enhanced human-system interaction (HSI) in maintenance processes. HSI can be enhanced by using perception-enhancing technology such as Augmented Reality (AR). However, the effective integration of AR into railway maintenance requires structured frameworks that address the challenges of task workflow, interaction, and information presentation. Hence, the purpose of this paper is to propose a framework for enabling AR-based assistance in railway maintenance. Following a Design Science Research (DSR) methodology, the framework defines functional elements and information flows to support the execution of inspection tasks and is realized through an AR-based system that integrates procedural guidance, flexible workflow navigation, multimodal interaction, contextual information access, and automated inspection log generation. A controlled user study compares the AR-assisted inspection approach with a traditional inspection method using standardized usability measures. The results indicate that both approaches scored within the marginal usability range. These findings indicate the feasibility of AR-based assistance as a human-centric complement to existing railway maintenance practices, while motivating further refinement and evaluation in operational settings.
Parul Khanna, P. Tretten, Ramin Karim· IEEE Access· 0 citations
The increasing complexity of smart production lines demands intelligent and lifecycle-aware approaches to operation and maintenance (O and M). Digital twin (DT) technology, which creates high-fidelity virtual replicas of physical systems, offers a promising paradigm for real-time monitoring, predictive maintenance, and continuous optimization. However, existing DT applications predominantly address isolated lifecycle phases without providing a unified framework. This paper proposes a Digital Twin-Driven Full Lifecycle O and M Framework (DT-FLOM) for smart production lines, encompassing four phases: virtual commissioning, real-time operational monitoring, predictive maintenance and health management, and continuous performance optimization. The framework is validated through retrospective analysis of publicly reported DT implementations in discrete manufacturing. This study provides a systematic reference for deploying DT-driven O and M strategies in Industry 4.0 smart production environments.
Yuejun Li, Guilan Xiao· Digital Signal and Computer...· 0 citations
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
This study aims to implement an Edge-IoT-based real-time predictive maintenance service platform to effectively support the full life cycle of maintenance, repair, and operation(MRO) for a hybrid regenerative thermal oxidizer(RTO). In distributed operating environments where remote RTO facilities and centralized monitoring systems are spatially separated, continuous condition monitoring, early identification of abnormal signs, and systematic response procedures are essential. Accordingly, this study seeks to enhance operational safety and maintenance efficiency by structuring failure types, root causes, and response procedures based on actual failure cases and representative operating scenarios of RTO facilities. To achieve this objective, an integrated platform was designed and implemented, consisting of field sensors, Edge-IoT gateway devices, an MQTT-based data collector, Redis and MySQL databases, a web server, a hybrid application, and a monitoring module. In addition, an analysis procedure considering the application of low-power edge devices was designed to enable independent condition assessment even under resource-constrained field environments. The target data included time-series variables reflecting both RTO operating conditions and emission characteristics, such as temperature, pressure, flow rate, current, vibration, and volatile organic compound(VOC) concentration. These data were preprocessed and utilized for abnormality detection and failure prediction. The implementation results confirmed that the proposed platform can collect and visualize the operating status of remote RTO facilities in near real time. Furthermore, when abnormal operating conditions occur, the platform supports rapid maintenance decision-making by providing alarms, storing historical records, and linking predefined response procedures. In particular, by integrating VOC emission characteristics with RTO operating conditions, the platform demonstrated its potential not only for improving facility safety but also for supporting emission reduction and environmental management. Moreover, the integration of Edge-IoT, lightweight AI-based analysis, and 3D digital twin-based monitoring represents a distinctive contribution compared with conventional remote monitoring systems that primarily focus on simple supervisory functions. In conclusion, this study presents a practical reference model for the digital transformation of RTO facilities and the establishment of a predictive maintenance framework. The proposed platform enables active condition assessment under limited field infrastructure and supports systematic responses to abnormal signs, thereby contributing to improved maintenance efficiency, operational safety, and environmental management capability. If further applied and expanded to various industrial RTO facilities, the proposed approach is expected to promote data-driven safety management and the wider adoption of predictive maintenance-oriented operational practices.
M. Jang, H. Park, Dal-Hwan Yoon et al.· Forum of Public Safety and C...· 0 citations