Empowering Predictive Maintenance of Medical Equipment Through AI-Driven Condition Monitoring DOI
Aminatul Saadiah Abdul Jamil, Azira Khalil,

Mardhiyati Mohd Yunus

и другие.

Series in bioengineering, Год журнала: 2024, Номер unknown, С. 53 - 68

Опубликована: Янв. 1, 2024

Язык: Английский

AI-Enabled Cognitive Predictive Maintenance of Urban Assets Using City Information Modeling—Systematic Review DOI Creative Commons
Oluwatoyin Lawal,

Nawari O. Nawari,

Omobolaji Lawal

и другие.

Buildings, Год журнала: 2025, Номер 15(5), С. 690 - 690

Опубликована: Фев. 22, 2025

Predictive maintenance of built assets often relies on scheduled routine practices that are disconnected from real-time stress assessment, degradation and defects. However, while Digital Twin (DT) technology within building urban studies is maturing rapidly, its use in predictive limited. Traditional preventive reactive strategies more prevalent facility management not intuitive, resource efficient, cannot prevent failure either underserve the asset or surplus to requirements. City Information Modeling (CIM) refers a federation BIM models accordance with real-world geospatial references, it can be deployed as an Urban (UDT) at city level, like BIM’s deployment level. This study presents systematic review 105 Scopus-indexed papers establish current trends, gaps opportunities for cognitive framework architecture, engineering, construction operations (AECO) industry. A UDT consisting CIM section University Florida campus proposed bridge knowledge gap highlighted review. The illustrates potential CNN-IoT integration improve through advance notifications. It also eliminates centralized information archiving.

Язык: Английский

Процитировано

1

Online behavioral matching for proton exchange membrane water electrolyzers: A digital twin approach DOI
Shaojie Liu, YangQuan Chen, Yongdong Wang

и другие.

Applied Energy, Год журнала: 2025, Номер 384, С. 125375 - 125375

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

0

Predictive Maintenance for Medical Equipment Using AI-Powered Digital Twins DOI

Sarika Tanaji Nikam,

Babasaheb Jadhav, Mily Lal

и другие.

IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 131 - 154

Опубликована: Май 2, 2025

With AI and Digital Twin technology, the way medical equipment maintenance is transformed into reliability improvement, minimization of downtime, an enhanced life span for critical healthcare assets, application will provide virtual models how devices function perform, with IoT sensor data analyzed using algorithms to predict prevent failures. This chapter provides methodology building digital twins, acquisition through sensors, AI-based predictive analytics predicting needs sudden breakdowns high costs repair. Results show that twins can achieve a degree prediction accuracy while optimizing schedules reducing downtime considerably. Comparative analysis also shows superior operational efficiency very stringent compliance. give organizations practical advice on implementing consistent performance patient care.

Язык: Английский

Процитировано

0

Digital twin technology in wind turbine components: A review DOI Creative Commons
Jersson X. Leon‐Medina, Diego Alexander Tibaduiza Burgos, Núria Parés Mariné

и другие.

Intelligent Systems with Applications, Год журнала: 2025, Номер unknown, С. 200535 - 200535

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Product digital twins: An umbrella review and research agenda for understanding their value DOI Creative Commons

Francisco Gomez Medina,

Veronica Martinez Hernandez

Computers in Industry, Год журнала: 2024, Номер 164, С. 104181 - 104181

Опубликована: Сен. 21, 2024

Язык: Английский

Процитировано

1

Advances in Hydraulic Transient-Based Pipeline Condition Assessment and Feature Diagnosis DOI
Jinzhe Gong, Tong‐Chuan Che, Wei Zeng

и другие.

˜The œhandbook of environmental chemistry, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Empowering Predictive Maintenance of Medical Equipment Through AI-Driven Condition Monitoring DOI
Aminatul Saadiah Abdul Jamil, Azira Khalil,

Mardhiyati Mohd Yunus

и другие.

Series in bioengineering, Год журнала: 2024, Номер unknown, С. 53 - 68

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0