Low-Frequency Ultrasound Thoracic Signal Processing Based on Music Algorithm and EMD Wavelet Thresholding

Zhou, Yinggang; Jifeng, LI; Yan, Hua; Yan, Xi

doi:10.1109/access.2023.3296465

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…Empirical mode decomposition (EMD), as an adaptive signal processing method that decomposes a time series into some limited intrinsic mode functions (IMFs). It has already operated in the areas of fault detection, signal processing and data compression [19][20][21]. However, due to the problems of endpoint effects and mode mixing in non-stationary signal decomposition, EMD is limited in practical applications.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis of GIS Partial Discharge Based on Improved Whale Optimization Algorithm

Sun,

Ma,

Wang

2024

IEEE Access

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Partial discharge (PD) seriously affects the operational safety of power equipment. In order to effectively diagnose the PD in gas insulated switchgear (GIS), a GIS PD fault diagnosis method based on improved whale optimization algorithm (IWOA) is proposed, which optimizes variational mode decomposition (VMD) and support vector machine (SVM) to adaptively determine the appropriate parameters and further enhance performance. A laboratory GIS PD platform is built to collect four types of PD fault signals (point discharge, particle discharge, floating discharge, and air-gap discharge). Firstly, a nonlinear arctangent convergence factor and adaptive weight are proposed to address the issue of local optimization in the WOA optimization process. Then, IWOA is used to optimize parameters of VMD (mode parameter K and penalty factor α). Next, effective intrinsic mode functions (IMFs) are screened through correlation coefficients which are greater than 0.2. Because a single scale cannot fully reflect all signal information, and more important information is distributed in other scales, multiscale permutation entropy (MPE) is introduced for feature extraction. Furthermore, the principal component analysis (PCA) method is employed for dimension reduction of initial feature vectors, which reduces the dimension of 33 feature vectors to 7. Finally, SVM based on IWOA is applied to train and test the experimental data to identify different types of PD faults, and achieve diagnosis of GIS PD. Through experimental analysis and comparison with other methods such as EMD-MPE, WOA-VMD-MSE, etc., the proposed method has good diagnostic effects. Also, it proves the robustness and feasibility of the presented solution. The optimization model provides a reference for solving fault diagnosis of GIS PD problems.

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Section: Introductionmentioning

confidence: 99%

A Novel Fault Diagnosis of GIS Partial Discharge Based on Improved Whale Optimization Algorithm

Sun,

Ma,

Wang

2024

IEEE Access

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“…Empirical mode de-composition (EMD) [13] can adaptively decompose the signal into a series of intrinsic mode functions (IMFs) through the local scale features of the signal, so as to reveal the internal properties of the signal. However, EMD has some problems such as endpoint effect and mode mixing [14][15]. Some new decomposition methods, such as feature mode decomposition (FMD) [16], it has been demonstrated that FMD has superiority in feature extraction of machinery fault.…”

Section: Introductionmentioning

confidence: 99%

Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge

Sun,

Ma,

Wang

2024

IEEE Access

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Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is proposed, which automatically optimizes parameters of variational mode decomposition (VMD) and support vector machine (SVM) to realize fault identification of GIS PD. Firstly, to overcome the shortcomings that NGO is easy to fall into local optimal solution and slow convergence speed, the opposite learning of refraction strategy and sine cosine algorithm (SCA) are introduced to optimize NGO. By comparing the test functions of various algorithms, the superiority of SCNGO algorithm is proved. Then, GIS PD experiment is designed for fault signal acquisition and algorithm verification. SCNGO-VMD is used for parameter adaptive optimization of PD signals. On this basis, the effective intrinsic mode functions (IMFs) are screened by composite index. Furthermore, time-domain, frequency-domain, and entropy features are constructed as mixed features and t-SNE is used to reduce the dimension. Finally, the feature vectors are input to SCNGO-SVM for fault identification. Through experimental analysis, compared with other algorithms, the proposed algorithm model has good state identification accuracy for GIS PD fault diagnosis. The paper provides a reference for the application of optimization algorithm in GIS PD fault identification .

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The application of a novel clutter removal algorithm to SAR beamforming in breast microwave imaging

Awasthi,

Jain

2025

Biomedical Signal Processing and Control

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Low-Frequency Ultrasound Thoracic Signal Processing Based on Music Algorithm and EMD Wavelet Thresholding

Cited by 4 publications

References 41 publications

A Novel Fault Diagnosis of GIS Partial Discharge Based on Improved Whale Optimization Algorithm

A Novel Fault Diagnosis of GIS Partial Discharge Based on Improved Whale Optimization Algorithm

Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge

The application of a novel clutter removal algorithm to SAR beamforming in breast microwave imaging

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