Research on noise reduction method for ship radiate noise based on secondary decomposition

Li, Guohui; Bu, Wenjia; Yang, Huirong

doi:10.1016/j.oceaneng.2022.113412

Cited by 25 publications

(4 citation statements)

References 38 publications

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“…Their study showed that AAPE, which takes into account the mean and difference of the sample amplitudes, is more sensitive to the signal’s amplitude characteristics than PE, and is a powerful tool for segmenting signals and detecting spikes. At present, AAPE has been used for image classification [ 37 ], feature extraction [ 38 , 39 , 40 ], and has been widely used in the mechanical field, especially in rotating machinery fault diagnosis [ 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang,

Li,

2024

Sensors

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In structural health monitoring (SHM), most current methods and techniques are based on the assumption of linear models and linear damage. However, the damage in real engineering structures is more characterized by nonlinear behavior, including the appearance of cracks and the loosening of bolts. To solve the structural nonlinear damage diagnosis problem more effectively, this study combines the autoregressive (AR) model and amplitude-aware permutation entropy (AAPE) to propose a data-driven damage detection method. First, an AR model is built for the acceleration data from each structure sensor in the baseline state, including determining the model order using a modified iterative method based on the Bayesian information criterion (BIC) and calculating the model coefficients. Subsequently, in the testing phase, the residuals of the AR model are extracted as damage-sensitive features (DSFs), and the AAPE is calculated as a damage classifier to diagnose the nonlinear damage. Numerical simulation of a six-story building model and experimental data from a three-story frame structure at the Los Alamos Laboratory are utilized to illustrate the effectiveness of the proposed methodology. In addition, to demonstrate the advantages of the present method, we analyzed AAPE in comparison with other advanced univariate damage classifiers. The numerical and experimental results demonstrate the proposed method’s advantages in detecting and localizing minor damage. Moreover, this method is applicable to distributed sensor monitoring systems.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang,

Li,

2024

Sensors

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“…After its first development, it has been widely used in many branches of science and engineering to analyze nonstationary or nonlinear signals [10]. In most situations, this technique is used for the earth sciences [11,12] but currently, the generality of EMD and HHT can be seen in various fields such as medicines [13], speech recognition [14], quantum systems [15], ocean engineering [16] and finance [17]. Keeping in view the versatile characteristics of EMD, it is probable that this method will find new ways to solve problems in almost every field of science that contains experimental data.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Empirical Mode Decomposition Based on Akima Spline Interpolation Technique

et al. 2023

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The objective of this research work is to extend the scope of empirical mode decomposition (EMD) algorithm, as an efficient tool to decompose the nonlinear and non-stationary time series. For EMD to be widely applicable, the extension utilizes both clean and noisy data sets. When constructing upper and lower envelopes, the proposed algorithm utilizes the Akima spline interpolation technique rather than a cubic spline. The proposed EMD is called Akima-EMD, which is used to identify non-informative fluctuations in the signal, such as noise, outliers, and ultra-high frequency components, and to breakdown the clean and chaotic data into various components avoiding distortion. It has been shown through the synthetic as well as real-world time series data analysis that the proposed method successfully extracts noise in the form of the first IMF from the data.INDEX TERMS Akima, Empirical mode decomposition (EMD), Fast Fourier transform (FFT), intrinsic mode functions (IMFs), Variational mode decomposition (VMD), complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN)

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“…Underwater acoustic target recognition (UATR) has always been a hot topic of research in the field of passive sonar and is also a technical problem that needs to be solved internationally, both in the civilian and military fields. Traditional UATR [1][2][3][4][5] still faces challenges such as unstable target features under complex conditions, interference from environmental noise and other targets, distortion of characteristics during acoustic propagation, etc. In recent years, with the development of artificial intelligence technology and the increase in public data on underwater acoustic signals [6,7], researchers have used deep learning models to establish the mapping relationship from the original data of the target category, using a data-driven network model which extracts non-linear features in a way that provides a new idea to solve the above problems.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Target Recognition Based on Deep Residual Attention Convolutional Neural Network

Ji,

Ni,

et al. 2023

JMSE

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Underwater acoustic target recognition methods based on time-frequency analysis have shortcomings, such as missing information on target characteristics and having a large computation volume, which leads to difficulties in improving the accuracy and immediacy of the target recognition system. In this paper, an underwater acoustic target recognition model based on a deep residual attention convolutional neural network called DRACNN is proposed, whose input is the time-domain signal of the underwater acoustic targets radiated noise. In this model, convolutional blocks with attention to the mechanisms are used to focus on and extract deep features of the target, and residual networks are used to improve the stability of the network training. On the full ShipsEar dataset, the recognition accuracy of the DRACNN model is 97.1%, which is 2.2% higher than the resnet-18 model with an approximately equal number of parameters as this model. With similar recognition accuracies, the DRACNN model parameters are 1/36th and 1/10th of the AResNet model and UTAR-Transformer model, respectively, and the floating-point operations are 1/292nd and 1/46th of the two models, respectively. Finally, the DRACNN model pre-trained on the ShipsEar dataset was migrated to the DeepShip dataset and achieved recognition accuracy of 89.2%. The experimental results illustrate that the DRACNN model has excellent generalization ability and is suitable for a micro-UATR system.

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Research on noise reduction method for ship radiate noise based on secondary decomposition

Cited by 25 publications

References 38 publications

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

A New Approach to Empirical Mode Decomposition Based on Akima Spline Interpolation Technique

Underwater Acoustic Target Recognition Based on Deep Residual Attention Convolutional Neural Network

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