Abstract
Gearbox bearings play an important role in wind power generation system. Their regular and stable operation will increase wind turbine power generation and improve the economic efficiency of wind farms. They often fail because they work under complex wind conditions. Therefore, it is necessary to find the fault early. The vibration signal of the gearbox bearing has the characteristics of volatility and continuity. Traditional bearing fault diagnosis methods are often based on signal analysis and feature selection, and the process is relatively complex. Deep learning methods can extract and select features automatically, thereby reducing the workload. A fault diagnosis method based on deep learning is proposed in this study. This method combines a one-dimensional convolutional neural network (1DCNN), support vector machine (SVM) classifier, and 1DCNN adaptively extracts features. The extracted features are input into the SVM classifier, and particle swarm optimization (PSO) is used to optimize the SVM classifier. The results show that the proposed fault diagnosis method is effective for fault diagnosis of wind turbine gearbox bearings. This method improves the precision and accuracy of diagnosis when compared to other methods.
Highlights
In recent years, renewable energy has become increasingly important
It is necessary to choose an appropriate optimizer in the model for the bearing fault diagnosis of the wind turbine gearbox
A fault diagnosis method for the bearing of wind turbine gearbox based on 1DCNN-particle swarm optimization (PSO)-support vector machine (SVM) is proposed in the study
Summary
Renewable energy has become increasingly important. Renewable energy can play a role in protecting the ecological environment and alleviating the use of electrical energy. Wind turbines usually operate in places with relatively harsh environments, such as gobi, islands, and grasslands They are subject to wind impact all year round, the load is extremely unstable, and the temperature difference between day and night is large. Gearbox bearings are an important part of wind turbines, and their operating conditions affect the functions of the entire wind turbine equipment to a large extent. It plays a vital role in the process of power transmission. Since gearbox bearings play a key role in wind turbines, if a failure occurs, it is likely to cause major emergencies and huge economic losses, to ensure its safe and stable operation is of great importance. An in-depth study on the fault diagnosis of wind turbine bearings is of great importance for reducing maintenance time, increasing annual power generation, reducing the operating and maintenance costs of wind turbines, and improving the economic benefits of wind farms
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call