The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Condition Monitoring and Fault Detection With AI and Digital Twin Technologies
|
|
Author(s): R. N. Ravikumar (Marwadi University, Rajkot, India), S. Aarthi (Marwadi University, Rajkot, India), Shakhboz Meylikulov (Termez University of Economics and Service, Termez, Uzbekistan), C. Navamani (Nandha Engineering college, Erode, India), Bekzod Madaminov (Mamun University, Khiva, Uzbekistan)and T. M. Saravanan (Kongu Engineering College, Erode, India)
Copyright: 2026
Pages: 36
Source title:
AI-Powered Analysis, Modeling, and Monitoring of Wind Energy Systems
Source Author(s)/Editor(s): Jackson J. Justo (University of Dar es Salaam, Dar es Salaam, Tanzania & ITMO University, St. Petersburg, Russia), Galina Demidova (Hangzhou Dianzi University, China & ITMO University, St. Petersburg, Russia), Francis A. Mwasilu (University of Dar es Salaam, Tanzania), Dmitry V. Lukichev (ITMO University, Russia)and Ramesh C. Bansal (University of Sharjah, Sharjah, UAE & University of Pretoria, Pretoria, South Africa)
DOI: 10.4018/979-8-3373-4159-0.ch008
Purchase
|
Abstract
As the world moves toward renewable energy, wind power stands out as a key sustainable source. However, wind turbines face challenges in reliability due to harsh conditions, mechanical wear, and electrical stress. Traditional maintenance falls short for large wind farms. This chapter explores how Artificial Intelligence (AI) and Digital Twin technologies enable real-time monitoring of blades, gearboxes, bearings, generators, and converters. AI models like Neural Networks, Support Vector Machines, Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) help detect faults and predict failures early. Digital twins enhance diagnostics by simulating turbine behavior using real-time data. The chapter also covers hybrid modeling, anomaly detection, and federated learning. Case studies include offshore wind AI, blade crack detection, and converter diagnostics. Challenges include data quality, cybersecurity, and scalability. Future trends point to edge AI, cloud-based diagnostics, and blockchain for secure monitoring and predictive maintenance.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|