Fault diagnosis for wind turbine based on improved extreme learning machine

Wu, Bin; Xi, Lifeng; Fan, Sixia; Zhan, Jian

doi:10.1007/s12204-017-1849-x

Cited by 11 publications

(14 citation statements)

References 11 publications

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“…To further verify the validity of the diagnostic model, this paper tests the 40 groups of wind turbine samples listed in [21]. Among the 40 groups of samples, each fault condition has 6 samples so that there are 240 sample data points and 8 failure categories.…”

Section: Simulation Experiments and Discussionmentioning

confidence: 99%

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Huang

Weihong

2018

Energies

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To investigate problems involving wind turbines that easily occur but are hard to diagnose, this paper presents a wind turbine (WT) fault diagnosis algorithm based on a spectrogram and a convolutional neural network. First, the original data are sampled into a phonetic form. Then, the data are transformed into a spectrogram in the time-frequency domain. Finally, the data are sent into a convolutional neural network (CNN) model with batch regularization for training and testing. Experimental results show that the method is suitable for training a large number of samples and has good scalability. Compared with Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and other fault diagnosis methods, the average diagnostic correctness rate is higher; so, the method can provide more accurate reference information for wind turbine fault diagnosis.

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Section: Simulation Experiments and Discussionmentioning

confidence: 99%

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Huang

Weihong

2018

Energies

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“…The most widely known type is the Moore-Penrose pseudo-inverse [35]. The pseudo-inverse of the matrix , namely , can be computed via least square solution: (14) This solution is accurate as long as the square matrix is invertible. However, it is singular in some applications.…”

Section: Extreme Learning Machine Elmmentioning

confidence: 99%

“…The predictor has been used for real-time to enhance the dynamic security of the power systems. Article [14] explores a fault diagnosis method for the key mechanical components of the wind turbine. The method based on improved extreme learning machine (IELM) combined the fault characteristics of gear and bearing of wind turbine drive system.…”

Section: Introductionmentioning

confidence: 99%

Power flow variation based on extreme learning machine algorithm in power system

Imen

Djamel

2019

IJPEDS

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<p>The main focus of this paper is a study that empowers us to understand how the temperature variation affects the transmission line resistance and as a result the power flow analysis with a specific end goal to assess losses in the electrical network. The paper is composed of two sections; the first part is a power flow study under normal conditions utilizing the neural network approach while the second investigated extreme learning machine algorithm efficiency and exactitude. Extreme learning machine algorithm has been used to settle several complications in power system: load forecasting, fault diagnosis, economic dispatch, security, transient stability; Thus, we proposed to study this technique to figure out this sort of complex issue.</p>The study was conducted for IEEE 30 bus test system. The simulation results are exposed and analyzed in detail at the end of this paper.

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“…As a key technology, motion recognition technology has been paid more and more attention by scholars, and many schools, research institutions, and scholars have invested in this work and achieved some success, such as MIT, Carnegie Mellon University Colleges, and universities, and have established specialized research laboratories to carry out this research work. [15][16][17] This requires extracting each frame of image from the video and then recognizing the human body from the image, further recognizing the human motion. 18 Therefore, estimating a person's complete three-dimensional (3-D) pose from an RGB image is one of the most challenging problems in computer vision.…”

Section: Introductionmentioning

confidence: 99%

Human motion recognition based on limit learning machine

Chen

Zhao

et al. 2020

International Journal of Advanced Robotic Systems

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With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

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Fault diagnosis for wind turbine based on improved extreme learning machine

Cited by 11 publications

References 11 publications

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System

Power flow variation based on extreme learning machine algorithm in power system

Human motion recognition based on limit learning machine

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