Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

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

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)

A peer-reviewed article of this Preprint also exists.

Zheng, Y.; Wang, T.; Xin, B.; Xie, T.; Wang, Y. A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine. Sensors 2019, 19, 826. Zheng, Y.; Wang, T.; Xin, B.; Xie, T.; Wang, Y. A Sparse Autoencoder and Softmax Regression Based Diagnosis Method for the Attachment on the Blades of Marine Current Turbine. Sensors 2019, 19, 826.

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 is important and difficult 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. 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 the other techniques, experiment results show that the proposed method can diagnose the blade attachment with higher accuracy.

Keywords

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

Subject

Engineering, Marine Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.