Blade imbalanced fault diagnosis for marine current turbine based on sparse autoencoder and softmax regression

Because of the abundance of seston under the sea, the attachment on the blade of the marine current turbine (MCT) would cause imbalanced fault. In order to detect the imbalanced fault as soon as possible, an imbalanced fault characteristics analysis method is applied based on image processing. A dia...

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Vydáno v:2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC) s. 246 - 251
Hlavní autoři: Wen, Pingping, Wang, Tianzhen, Xin, Bin, Tang, Tianhao, Wang, Yide
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2018
Témata:
Biological neural networks
blade imbalanced fault
Blades
Feature extraction
marine current turbine
Neurons
softmax regression
sparse autoencoder
Torque
Wind turbines
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Shrnutí:Because of the abundance of seston under the sea, the attachment on the blade of the marine current turbine (MCT) would cause imbalanced fault. In order to detect the imbalanced fault as soon as possible, an imbalanced fault characteristics analysis method is applied based on image processing. A diagnosis method combining the modified sparse autoencoder (SA) and softmax regression (SR) is applied to process images and detect the imbalanced fault on the blade of MCT. The modified SA is used to extract the features and SR is used to classify them. The data of images are used to monitor whether the blade is attached by benthos and its corresponding degree of imbalance. Experiments show that the applied diagnosis method can achieve higher accuracy in the application of diagnosis of blade imbalanced fault compared with the traditional PCA feature extraction algorithm.
DOI:10.1109/YAC.2018.8406380