Extraction and classification of acoustic scattering from underwater target based on Wigner-Ville distribution

Wu, Yushuang; Li, Xiukun; Wang, Yan

doi:10.1016/j.apacoust.2018.03.026

Cited by 33 publications

(14 citation statements)

References 20 publications

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“…However, each representative method has some inherent defects in the extraction of non-stationary signal information. For example, WVD has unavoidable cross-interference terms, which has become an obstacle to its widespread application in signal processing [ 9 ]. The wavelet transform can divide the frequency band into multiple layers, but it cannot further decompose the high frequency part, and the selection of the wavelet base has a significant influence on the decomposition effect, and the adaptive ability is insufficient [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy

Song

Yao

et al. 2019

Entropy

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The normal operation of high-pressure common rail injector is one of the important prerequisites for the healthy and reliable operation of diesel engines. Therefore, this paper studies the high-precision fault diagnosis method for injectors. Firstly, this paper chooses VMD to adaptively decompose the common rail fuel pressure wave. The biggest difficulty in VMD decomposition is the need to manually set the internal combination parameters K and α. In order to overcome this shortcoming, this paper proposes an improved fruit fly search. The variational mode decomposition method of the algorithm, with the energy growth factor e as the objective function, can adaptively decompose the multi-component signal into superimposed sub-signals. In addition, based on the analytic hierarchy process and dispersion entropy, hierarchical dispersion entropy is proposed to obtain a comprehensive and accurate complexity estimation of time series. Then, a fault diagnosis scheme for high-pressure common rail injector based on IFOA-VMD and HDE is proposed. Finally, using the engineering test data, the method is compared with other methods. The proposed method appears, based on the numerical examples, to be better from both a computational and classification accuracy point of view.

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Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy

Song

Yao

et al. 2019

Entropy

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“…It has been difficult to implement continuous monitoring and recognition [1,2]. Therefore, underwater acoustic target recognition with a high recognition accuracy and efficiency attracts extensive attention both in military and civil fields [3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

2019

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A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

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“…However, the time and frequency domain methods are unsuitable in fault diagnosis in the case of non-stationary and non-linear vibration signals [6]. Time-frequency domain methods, such as wavelet transform [7] and Wigner–Ville distribution [8], have been used to extract the fault feature of rolling bearings. However, these methods cannot obtain the ideal time-frequency resolution subject to inherent cross-interference items [9] and Heisenberg’s uncertainty principle [10].…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features

Zhan

Zhang

et al. 2019

Sensors

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In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods.

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Extraction and classification of acoustic scattering from underwater target based on Wigner-Ville distribution

Cited by 33 publications

References 20 publications

Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy

Fault Diagnosis Method for High-Pressure Common Rail Injector Based on IFOA-VMD and Hierarchical Dispersion Entropy

Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features

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