Incorporating Machine Learning into Vibration Detection for Wind Turbines

J Vives,Nicolò Vaiana

doi:10.1155/2022/6572298

Abstract

With machine learning techniques, wind turbine components can be detected and diagnosed in advance, so degeneration can be prevented. Automatic and autonomous learning is used to predict, detect, and diagnose electrical and mechanical failures in wind turbines. Based on the implementation of machine learning algorithms adapted to the different components and faults of wind turbines, this study evaluates different methodologies for monitoring, supervision, and fault diagnosis.

Highlights

Due to global warming and the significant increase in energy demand and consumption, the transition to obtaining electricity from renewable sources is accelerating
In maintenance of wind turbines [1], the main fault methodologies are spectral analysis and fault trees, but with all the additional technological advances that this entails, connectivity, smart, and data generation, we are seeing a transformation in maintenance towards artificial intelligence (AI) and machine learning
This study illustrates how the use of machine learning techniques can allow for greater accuracy and prediction of possible breakages or anomalies by using the characteristics extracted from vibration measurements

Summary

Introduction

Due to global warming and the significant increase in energy demand and consumption, the transition to obtaining electricity from renewable sources is accelerating. The reliability, safety, and profitability of wind turbines can be improved with efficient methods of advanced monitoring and fault diagnosis. In maintenance of wind turbines [1], the main fault methodologies are spectral analysis and fault trees, but with all the additional technological advances that this entails, connectivity, smart, and data generation, we are seeing a transformation in maintenance towards artificial intelligence (AI) and machine learning. Data are available to the industry at this point, which affects critical decisions in crucial areas such as scheduling [2], maintenance management [3], and quality improvement [4]. Cloud-based solutions, and a new generation of algorithms have amplified the impact of machine learning in these areas [5]. There are many components in a wind turbine that work together, and vibration is one of the primary causes of the system’s failure

Methods

Results

Conclusion