A diagnosis method based on depthwise separable convolutional neural network for the attachment on the blade of marine current turbine

Abstract

To diagnose the attachment of marine current turbine, this article proposes a method based on convolutional neural network and the concepts of depthwise separable convolution to achieve feature extraction. The method consists of three steps: data preprocessing, feature extraction and fault diagnosis. This method can diagnose the fault degree of blade imbalance and uniform attachment in underwater environment with strong currents and complex spatiotemporal variability. It can extract distinct image feature in harsh marine environments by using a convolutional neural network. In addition, this method is robust for the recognition of blurred pictures under high-speed rotation.

IRNLGD: An Edge Detection Algorithm with Comprehensive Gradient Directions for Tidal Stream Turbine

“…The data collection experiment is performed on the marine current power generation system experimental platform, and the structure and parameters are the same as Ref. [18]. The rope is used to wrap around the TST rotor to simulate the biofouling, as shown in Figure 2.…”

“…However, they only focused on TSTs in a static state, which is an idealized condition not reflective of reality. Therefore, Xin et al [18] collected TST images under operational conditions to make the dataset more representative of real-world scenarios. Then, the data were classified using a depthwise separable convolutional neural network (CNN), which achieved higher recognition accuracy than SAE+SR and a reasonable computational cost compared to large deep networks such as ResNet.…”

Tidal Stream Turbine Biofouling Detection and Estimation: A Review-Based Roadmap

“…The significance of visual inspection for TSTs to ensure their safe and efficient operation has led to the proposal of using image processing as an effective alternative for monitoring biofouling. To achieve this, researchers have recommended using machine learning to extract features from processed images captured by an ROV [61][62][63][64]. However, several challenges have arisen in this context, particularly with regard to diagnosing biofouling types and estimating their thickness.…”

A Mini Review of Research on Blade Fault Detection of Marine Current Turbine

“…4(5).2021 as valuable fault detection information [10]. The reference [11] proposed a diagnosis method based on a deep separable convolutional neural network for the biofouling on the blades of MCTs. The status is identified through the fouled part and different pixels of the blade.…”