Rotor Fault Diagnosis of SCIG-Wind Turbine Using Hilbert Transform

2017 ◽  
Author(s):  
Lahcene Noureddine ◽  
Ahmed Hafaifa ◽  
Abdellah Kouzou
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Hilbert Transform

Development of Fault Diagnosis Classification Method System using Mahalanobis Distance in Micro Wind Turbine

2015 ◽  
Vol 135 (9) ◽  
pp. 577-578
Author(s):  
Toshio Bouno ◽  
Shinichi Arimori ◽  
Toshifumi Yuji ◽  
Hiroyuki Kinoshita
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Mahalanobis Distance ◽  
Classification Method ◽  
Diagnosis Classification

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

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.

scholarly journals A Fault Diagnosis Algorithm for Wind Turbine Blades Based on BP Neural Network

2021 ◽  
Vol 1043 (2) ◽  
pp. 022032
Author(s):  
Jun-Xi Bi ◽  
Wen-Ze Fan ◽  
Ying Wang ◽  
Jun Ren ◽  
Hai-Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Turbine Blades ◽  
Wind Turbine Blades ◽  
Diagnosis Algorithm

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

Industrial Fault Diagnosis using Hilbert Transform and Texture Features

2021 ◽  
Author(s):  
Mahe Zabin ◽  
Ho-Jin Choi ◽  
Jia Uddin ◽  
Md. Hasan Furhad ◽  
Abu.Barkat Ullah
Keyword(s):  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Texture Features
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