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

Yilai Zheng; Tianzhen Wang; Bin Xin; Tao Xie; Tianzhao Tang; Yide Wang

doi:10.20944/preprints201811.0394.v3

Preprint Article Version 3 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 11 November 2018 / Approved: 16 November 2018 / Online: 16 November 2018 (09:30:04 CET)
Version 2 : Received: 3 February 2019 / Approved: 4 February 2019 / Online: 4 February 2019 (13:22:39 CET)
Version 3 : Received: 9 February 2019 / Approved: 12 February 2019 / Online: 12 February 2019 (09:59:09 CET)

The development and application of marine current energy are attracting more and more attention around the world. Due to the hardness of its working environment, it is important and difficult to study the fault diagnosis of a marine current generation system. In this paper, an underwater image is chosen as the fault-diagnosing signal, after different sensors are compared. This paper proposes a diagnosis method based on the sparse autoencoder (SA) and softmax regression (SR). The 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 other methods, the experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

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

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

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