Transforming high-rise residential maintenance: a 3D spatio-temporal model utilizing Industry 4.0 and lean principles
Журнальна стаття Scopus WoS
Usman Mehmood, Uznir Ujang, Suhaibah Azri, Tan Liat Choon
<jats:sec><jats:title content-type="abstract-subheading">Purpose The purpose of this paper is to develop and demonstrate a comprehensive 3D spatio-temporal maintenance management model for high-rise residential buildings by integrating Industry 4.0 technologies and lean maintenance principles. This model aims to optimize maintenance scheduling, enhance resource utilization and improve decision-making processes. By leveraging advanced data visualization and predictive analytics, this study seeks to address the complexities of building maintenance, ensure timely interventions, reduce downtime and extend the lifespan of building assets, ultimately leading to more efficient and sustainable maintenance management practices. </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach Integrating state-of-the-art technologies such as big data analytics and artificial intelligence into the proposed model is geared towards benefiting from optimized maintenance scheduling and resource allocation, hence achieving minimum asset downtime and extension in asset life. This is being done through the digitization of paper maps, the development of 3D building models in AutoCAD and SketchUp and the placing of the developed models into ArcGIS Pro. The PostgreSQL database with PostGIS extension supports optimal storage and management of spatial data towards real-time updates and advanced analyses. </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Findings The results revealed that the model enhances maintenance planning considerably better than traditional methods due to the revelation of meaningful patterns and trends that are not visible in conventional visualization methods. Temporal analysis indicates increasing needs for maintenance through time, whereas spatial analysis can point out the units that require special attention. The spatiotemporal analysis is needed to determine overall maintenance requirements for better decision-making. The work demonstrated that 3D visualization of maintenance activities performed over building representation helps facility managers in better decision-making related to task planning for performance improvement concerning building and tenant satisfaction. </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Research limitations/implications The study’s current limitations include the reliance on specific datasets and technologies, which may need adaptation for broader applications. Future research could explore further integration with additional building types and longitudinal studies to assess long-term impacts. </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Practical implications The 3D visualization of maintenance activities over building representation aids facility managers in better decision-making related to task planning, improving building performance and tenant satisfaction. This integrated approach provides significant benefits in efficiency, resource use and sustainability. </jats:sec> <jats:sec><jats:title content-type="abstract-subheading">Originality/value The originality of this paper lies in its innovative integration of 3D spatio-temporal data with Industry 4.0 technologies and lean maintenance principles to create a comprehensive maintenance management model for high-rise residential buildings. Unlike traditional approaches, this model combines advanced data visualization, real-time analytics and predictive maintenance strategies within a unified geographic information system framework. This holistic approach not only enhances maintenance planning and resource allocation but also provides a proactive, data-driven methodology that significantly improves the efficiency and effectiveness of maintenance management, addressing the unique challenges of high-rise residential building maintenance. </jats:sec>
2024, Facilities, №1/2, с.32-53
Toward Enhanced Efficiency: Soft Sensing and Intelligent Modeling in Industrial Electrical Systems
Журнальна стаття Scopus WoS
Paul Arévalo, Danny Ochoa-Correa
This review article focuses on applying operation state detection and performance optimization techniques in industrial electrical systems. A comprehensive literature review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology to ensure a rigorous and transparent selection of high-quality studies. The review examines in detail how soft sensing technologies, such as state estimation and Kalman filtering, along with hybrid intelligent modeling techniques, are being used to enhance efficiency and reliability in the electrical industry. Specific case studies are analyzed in areas such as electrical network monitoring, fault detection in high-voltage equipment, and energy consumption optimization in industrial plants. The PRISMA methodology facilitated the identification and synthesis of the most relevant studies, providing a robust foundation for this review. Additionally, the article explores the challenges and research opportunities in applying these techniques in specific industrial contexts, such as steel metallurgy and chemical engineering. By incorporating findings from meticulously selected studies, this work offers a detailed, engineering-oriented insight into how advanced technologies are transforming industrial processes to achieve greater efficiency and operational safety.
DOI: 10.3390/pr12071365 
2024, Processes, №7, с.1365
Bibliometric Analysis of Reconfigurable Antennas in Radar Applications
Глава книги Scopus
Duygu Nazan Gençoğlan
This study conducts a comprehensive bibliometric analysis of research articles on reconfigurable antennas in radar applications retrieved from Google Scholar databases from 1989 to 2024. A total of 980 research articles are analysed, demonstrating significant interest and commitment to the topic, as evidenced by a total of 22.073 citations. Important metrics such as citations per year (649.21), citations per article (22.52), and citations per author (9,529). It highlight the importance and impact of research in this area. Collaboration is common, with an average of 410.95 articles per author and about three authors per article. The geographical analysis highlights the contributions of institutions in the United States and Switzerland. The study identifies emerging trends and future directions and highlights the need for dynamic antenna reconfiguration techniques, integration challenges, and energy-efficient solutions. The bibliometric analysis gives valuable insights into the current investigation landscape on reconfigurable antennas for radar applications.
2024, Advances in Wireless Technologies and Telecommunication Radar and RF Front End System Designs for Wireless Systems, с.57-91
Non-Invasive Self-Adaptive Information States’ Acquisition inside Dynamic Scattering Spaces
Журнальна стаття Scopus WoS
Ruifeng Li, Jinyan Ma, Da Li, Yunlong Wu, Chao Qian, Ling Zhang, Hongsheng Chen, Tsampikos Kottos, Er-Ping Li
Pushing the information states’ acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states’ acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework’s efficacy, we demonstrate that efficient information states’ acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.
2024, Research
On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture
Журнальна стаття Scopus WoS
Yanpeng Hao, Lida Zhu, Shaoqing Qin, Xiaoyu Pei, Tianming Yan, Qiuyu Qin, Hao Lu, Boling Yan
Abstract On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.
2024, International Journal of Extreme Manufacturing, №6, с.065602
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