Time-varying mesh stiffness modelling of offshore wind gearboxes under multi-fault modes

With the development of more complex offshore wind power generation structures, the operational stability and reliability of wind turbine units have gradually begun to confront significant challenges, particularly concerning gearbox failure issues. Notably, constructing fault datasets has been a persistent challenge due to problems such as inefficiency and exorbitant costs. To address this issue, this study proposes an advanced modelling and analysis method for time-varying gear meshing stiffness, aiming to enhance the accuracy and efficiency of fault dataset construction. To improve the simulation model’s ability to reflect real-world operating conditions, a multi-degree-of-freedom translational–torsional coupled dynamic model of a wind turbine gearbox under multiple non-linear factors was established using the Lagrangian equation. Unlike traditional single-fault models, this study also introduces an analysis method for time-varying gear meshing stiffness under multi-fault composite conditions by integrating an improved energy analysis approach. The research findings indicate that the proposed time-varying gear meshing stiffness model and analysis method can effectively construct fault datasets and facilitate the observation of fault characteristics.

Authors

Institutions

Publication Details

Journal
Proceedings of the Institution of Civil Engineers - Maritime Engineering
Published
2025-11-24
DOI
https://doi.org/10.1680/jmaen.25.00031
Citations
1
Primary Topic
Gear and Bearing Dynamics Analysis
Type
article
Field-Weighted Citation Impact
0.71
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Time-varying mesh stiffness modelling of offshore wind gearboxes under multi-fault modes

Tiago Fazeres-Ferradosa, Debiao Meng, Yipeng Guo, Hengfei Yang et al.
1 citations
Proceedings of the Institution of Civil Engineers - Maritime Engineering
Gear and Bearing Dynamics Analysis
0.71
article

Time-varying mesh stiffness modelling of offshore wind gearboxes under multi-fault modes

Tiago Fazeres-Ferradosa, Debiao Meng, Yipeng Guo, Hengfei Yang, Shun‐Peng Zhu, Muhammad Kamran Khan
article en
1 citations

Abstract

With the development of more complex offshore wind power generation structures, the operational stability and reliability of wind turbine units have gradually begun to confront significant challenges, particularly concerning gearbox failure issues. Notably, constructing fault datasets has been a persistent challenge due to problems such as inefficiency and exorbitant costs. To address this issue, this study proposes an advanced modelling and analysis method for time-varying gear meshing stiffness, aiming to enhance the accuracy and efficiency of fault dataset construction. To improve the simulation model’s ability to reflect real-world operating conditions, a multi-degree-of-freedom translational–torsional coupled dynamic model of a wind turbine gearbox under multiple non-linear factors was established using the Lagrangian equation. Unlike traditional single-fault models, this study also introduces an analysis method for time-varying gear meshing stiffness under multi-fault composite conditions by integrating an improved energy analysis approach. The research findings indicate that the proposed time-varying gear meshing stiffness model and analysis method can effectively construct fault datasets and facilitate the observation of fault characteristics.

Proceedings of the Institution of Civil Engineers - Maritime EngineeringVol. 178(4)
University of Electronic Science and Technology of China (CN), Universidade do Porto (PT)
Openalex Percentile: Top 25%
Gear and Bearing Dynamics Analysis
0.71
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.