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

Zheng, Yilai; Wang, Tianzhen; Xin, Bin; Xie, Tao; Wang, Yide

doi:10.3390/s19040826

Cited by 45 publications

(10 citation statements)

References 48 publications

(57 reference statements)

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“…Among them, the combination of the four key parameters of SVM, weight coefficient, penalty factor, radial base kernel function parameters, and insensitive loss function parameters were [0.08, 2.80, 0.58, 0.01] [ 47 ]. (6) The feature samples constructed in this paper were directly input into the Softmax layer with a classification function for evaluation (M6) [ 48 ]. The parameter settings and other processes are consistent with the method in this paper.…”

Section: Results Analysis and Methods Comparisonmentioning

confidence: 99%

Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

Liu

Kang

Wen

et al. 2022

Sensors

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Regarding the problem of the valve gap health status being difficult to assess due to the complex composition of the condition monitoring signal during the operation of the diesel engine, this paper proposes an adaptive noise reduction and multi-channel information fusion method for the health status assessment of diesel engine valve clearance. For the problem of missing fault information of single-channel sensors in condition monitoring, we built a diesel engine valve clearance preset simulation test bench and constructed a multi-sensor acquisition system to realize the acquisition of diesel engine multi-dimensional cylinder head signals. At the same time, for the problem of poor adaptability of most signal analysis methods, the improved butterfly optimization algorithm by the bacterial foraging algorithm was adopted to adaptively optimize the key parameter for variational mode decomposition, with discrete entropy as the fitness value. Then, to reduce the uncertainty of artificially selecting fault characteristics, the characteristic parameters with a higher recognition degree of diesel engine signal were selected through characteristic sensitivity analysis. To achieve an effective dimensionality reduction integration of multi-channel features, a stacked sparse autoencoder was used to achieve deep fusion of the multi-dimensional feature values. Finally, the feature samples were entered into the constructed one-dimensional convolutional neural network with a four-layer parameter space for training to realize the health status assessment of the diesel engine. In addition, we verified the effectiveness of the method by carrying out valve degradation simulation experiments on the diesel engine test bench. Experimental results show that, compared with other common evaluation methods, the method used in this paper has a better health state evaluation effect.

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Section: Results Analysis and Methods Comparisonmentioning

confidence: 99%

Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

Liu

Kang

Wen

et al. 2022

Sensors

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“…They focus on classification and semantic segmentation networks. Zheng et al [17] carried out attachment detection using an improved sparse autoencoder (SAE) and Softmax Regression (SR) method. They collected TST attachment images and divided the data based on the degree of attachment.…”

Section: Introductionmentioning

confidence: 99%

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

Song,

Liu,

Zhang

et al. 2024

JMSE

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Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting the grid. Therefore, rotor condition monitoring is of great significance for the stable operation of the system. Image-based attachment detection algorithms provide the advantage of visually displaying the location and area of faults. However, due to the limited availability of data from multiple machine types and environments, it is difficult to ensure the generalization of the network. Additionally, TST images degrade, resulting in reduced image gradients and making it challenging to extract edge and other features. In order to address the issue of limited data, a novel non-data-driven edge detection algorithm, indexed resemble-normal-line guidance detector (IRNLGD), is proposed for TST rotor attachment fault detection. Aiming to solve the problem of edge features being suppressed, IRNLGD introduces the concept of “indexed resemble-normal-line direction” and integrates multi-directional gradient information for edge determination. Real-image experiments demonstrate IRNLGD’s effectiveness in detecting TST rotor edges and faults. Evaluation on public datasets shows the superior performance of our method in detecting fine edges in low-light images.

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“…The fault detection method was also applied based on the motor speed signature [21]. Image signals obtained by an underwater camera were used to detect blade imbalance fault in an MCT in [22,23]. However, these sensors need to be waterproof and compression-resistant, which are difficult to install under complex conditions.…”

Section: Introductionmentioning

confidence: 99%

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

Wei

Xie

Shi

et al. 2021

JMSE

Self Cite

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Marine current energy as a kind of renewable energy has gradually attracted more and more attention from many countries. However, the blade imbalance fault of marine current turbines (MCTs) will have an effect on the power production efficiency and cause damage to the MCT system. It is hard to classify the severity of an MCT blade imbalance fault under the condition of the current instability and seafloor noise. This paper proposes a fault classification method based on the combination of variational mode decomposition denoising (VMD denoising) and screening linear discriminant analysis (S-LDA). The proposed method consists of three parts. Firstly, phase demodulation of the collected stator current signal is performed by the Hilbert transform (HT) method. Then, the obtained demodulation signal is denoised by variational mode decomposition denoising (VMD denoising), and the denoised signal is analyzed by power spectral density (PSD). Finally, S-LDA is employed on the power signal to determine the severities of fault classification. The effectiveness of the proposed method is verified by experimental results under different severities of blade imbalance fault. The stator current signatures of experiments with different severities of blade imbalance fault are used to validate the effectiveness of the proposed method. The fault classification accuracy is 92.04% based on the proposed method. Moreover, the experimental results verify that the influence of velocity fluctuation on fault classification can be eliminated.

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

Cited by 45 publications

References 48 publications

Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

Health Status Assessment of Diesel Engine Valve Clearance Based on BFA-BOA-VMD Adaptive Noise Reduction and Multi-Channel Information Fusion

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

Imbalance Fault Classification Based on VMD Denoising and S-LDA for Variable-Speed Marine Current Turbine

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