Abstract
The marine current turbine has gradually entered and contributed to world energy resources. However, the growth of marine organisms or marine pollutants also cause imbalance faults of blades, and it consequently damages other components of the marine current turbine, such as generators, bearings, and winding insulation systems. Thus, it is important to detect and identify the imbalance faults of blades in time. There are two fault detection methods introduced by an electrical signal in this chapter: the Hilbert transform-based imbalance fault detection method using the stator voltage, and the wavelet threshold denoising–based imbalance fault detection method using the stator current. Because the electrical signal cannot be used to diagnose the uniform faults and symmetrical faults, there are two identification methods introduced by the image signal in this chapter: blade attachment based on the sparse autoencoder and softmax regression, and blade attachment based on a depthwise separable convolutional neural network.
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