A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis This article reviews the state-of-the-art condition monitoring technologies used for fault diagnosis and lifetime prognosis in wind turbines. By HAMED BADIHI , Member IEEE, YOUMIN ZHANG , Senior Member IEEE,BIN J IANG , Fellow IEEE, PRAGASEN PILLAY , Fellow IEEE, AND SUBHASH RAKHEJA ABSTRACT | Wind turbines play an increasingly important role in renewable power generation. To ensure the efficient produc- tion and financial viability of wind power, it is crucial to main- tain wind turbines’ reliability and availability (uptime) through advanced real-time condition monitoring technologies. Given their plurality and evolution, this article provides an updated comprehensive review of the state-of-the-art condition moni- toring technologies used for fault diagnosis and lifetime prog- nosis in wind turbines. Specifically, this article presents the Manuscript received March 1, 2022; accepted April 19, 2022. Date of publication May 13, 2022; date of current version June 3, 2022. This work was supported in part by the National Natural Science Foundation of China under Grant 62003166 and Grant 61833013, in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Project Grants, in part by the Team Start-Up and Accelerator Grants of Concordia University, and in part by the Seed Fund of Concordia University. (Corresponding author: Youmin Zhang.) Hamed Badihi and Bin Jiang are with the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing 211106, China (e-mail: hamed.badihi@nuaa.edu.cn; binjiang@nuaa.edu.cn). Youmin Zhang and Subhash Rakheja are with the Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada (e-mail: youmin.zhang@concordia.ca; subhash.rakheja@concordia.ca). Pragasen Pillay is with the Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada (e-mail: pragasen.pillay@concordia.ca). Digital Object Identifier 10.1109/JPROC.2022.3171691 major fault and failure modes observed in wind turbines along with their root causes, and thoroughly reviews the techniques and strategies available for wind turbine condition monitoring from signal-based to model-based perspectives. In total, more than 390 references, mostly selected from recent journal arti- cles, theses, and reports in the open literature, are compiled to assess as exhaustively as possible the past, current, and future research and development trends in this substantial and active investigation area. KEYWORDS | Condition monitoring; fault detection and diagno- sis (FDD); lifetime prognosis (LTP); wind farm; wind turbine. I. INTRODUCTION While wind power production keeps rising worldwide, wind turbines are playing an increasingly major role in the present and future of renewable power generation. Yet, in the current wind production landscape, two trends seem to jeopardize the fulfillment of this global role. On the one hand, a significant share of the existing wind turbines has already reached its 20-year estimated lifetime, which requires additional maintenance services; on the other hand, new wind turbine technology is evolving toward larger wind turbines in remote offshore locations, which 754 PROCEEDINGS OF THE IEEE | Vol. 110, No. 6, June 2022 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/