Machine learning methods for wind turbine condition monitoring: A review

By Adrian Stetco, F. Dinmohammadi, Xingyu Zhao, Valentin Robu, David Flynn, Mike Barnes, John A. Keane, Goran Nenadic. Renewable Energy, 2019

704 citations recorded by the source corpus.

Abstract This paper reviews the recent literature on machine learning (ML) models that have been used for condition monitoring in wind turbines (e.g. blade fault detection or generator temperature monitoring). We classify these models by typical ML steps, including data sources, feature selection and extraction, model selection (classification, regression), validation and decision-making. Our findings show that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression. Neural networks, support vector machines and decision trees are most commonly used. We conclude with a discussion of the main areas for future work in this domain.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/d8ad3afdf4480c646169b916e81573bd6eef6b17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Ruqiang Yan
2. Xuefeng Chen

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Shibin Wang
5. Zhibin Zhao

In Electrical and Electronic Engineering

1. Ruqiang Yan
2. Xuefeng Chen

Citations in the corpus

• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, background): “[25] reviewed machine learning approaches applied to wind turbine condition monitoring and made a discussion of the possibility for the future research.”