Wind turbine gearbox fault diagnosis based on EEMD and fractal theory

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

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Fault diagnosis of wind turbine gearbox using a novel method of fast deep graph convolutional networks

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2020.3048799 ◽

2021 ◽

pp. 1-1

Author(s):

Xiaoxia Yu ◽

Baoping Tang ◽

Kai Zhang

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbine Gearbox ◽

Convolutional Networks ◽

Novel Method

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Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information

Renewable Energy ◽

10.1016/j.renene.2021.06.088 ◽

2021 ◽

Author(s):

Sheng Guo ◽

Tao Yang ◽

Haochen Hua ◽

Junwei Cao

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Wind Turbine ◽

Convolutional Neural Networks ◽

Wind Turbine Gearbox ◽

Coupling Fault

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A novel wind turbine gearbox fault diagnosis method based on ASO-VMD and NRF

Journal of Vibroengineering ◽

10.21595/jve.2020.21316 ◽

2020 ◽

Vol 22 (6) ◽

pp. 1357-1372

Author(s):

Wenyi Liu ◽

Mengchen Shan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbine Gearbox ◽

Diagnosis Method

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Vestas V90-3MW Wind Turbine Gearbox Health Assessment Using a Vibration-Based Condition Monitoring System

Shock and Vibration ◽

10.1155/2016/6423587 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 7

Author(s):

A. Romero ◽

Y. Lage ◽

S. Soua ◽

B. Wang ◽

T.-H. Gan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Condition Monitoring ◽

Learning Algorithm ◽

Health Assessment ◽

Field Trials ◽

Health Condition ◽

Normal Operation ◽

Wind Turbine Gearbox ◽

Operation Conditions

Reliable monitoring for the early fault diagnosis of gearbox faults is of great concern for the wind industry. This paper presents a novel approach for health condition monitoring (CM) and fault diagnosis in wind turbine gearboxes using vibration analysis. This methodology is based on a machine learning algorithm that generates a baseline for the identification of deviations from the normal operation conditions of the turbine and the intrinsic characteristic-scale decomposition (ICD) method for fault type recognition. Outliers picked up during the baseline stage are decomposed by the ICD method to obtain the product components which reveal the fault information. The new methodology proposed for gear and bearing defect identification was validated by laboratory and field trials, comparing well with the methods reviewed in the literature.

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Wind turbine gearbox fault diagnosis based on an improved supervised autoencoder using vibration and motor current signals

Measurement Science and Technology ◽

10.1088/1361-6501/ac0741 ◽

2021 ◽

Author(s):

Shuai Yang ◽

Yu Wang ◽

Li Chuan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbine Gearbox ◽

Motor Current

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Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

Mathematical Problems in Engineering ◽

10.1155/2014/153656 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Shoubin Wang ◽

Xiaogang Sun ◽

Chengwei Li

Keyword(s):

Time Series ◽

Riemannian Manifold ◽

Fault Diagnosis ◽

Wind Turbine ◽

Covariance Matrix ◽

Multivariate Time Series ◽

Time Sequence ◽

Mechanical Equipment ◽

Wind Turbine Gearbox ◽

Diagnosis Method

As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.

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