Preprint Article Version 1 This version is not peer-reviewed

A Sparse Autoencoder and Softmax Regression based Diagnosis Method for the Attachment on the Blade of Marine Current Turbine

Version 1 : Received: 11 November 2018 / Approved: 16 November 2018 / Online: 16 November 2018 (09:30:04 CET)

How to cite: Xin, B.; Wang, T.; Zheng, Y.; Wang, Y. A Sparse Autoencoder and Softmax Regression based Diagnosis Method for the Attachment on the Blade of Marine Current Turbine. Preprints 2018, 2018110394 (doi: 10.20944/preprints201811.0394.v1). Xin, B.; Wang, T.; Zheng, Y.; Wang, Y. A Sparse Autoencoder and Softmax Regression based Diagnosis Method for the Attachment on the Blade of Marine Current Turbine. Preprints 2018, 2018110394 (doi: 10.20944/preprints201811.0394.v1).

Abstract

The development and application of marine current energy are attracting more and more attention in the world. Due to the hardness of its working environment, it’s important to study the fault diagnosis of marine current generation system. In this paper, underwater image is chosen as the fault diagnosing signal after different sensors are compared. The faults are set by simulating varying degrees of biological attachment in the actual working environment of marine current turbine (MCT). This paper proposes a diagnosis method based on the improved sparse autoencoder (SA) and softmax regression (SR). The improved SA is used to extract the features and SR is used to classify them. Images are used to monitor whether the blade is attached by benthos and to determine its corresponding degree of attachment. Compared with the other techniques, experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

Subject Areas

marine current turbine; blade attachment; sparse autoencoder; softmax regression

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