Analysis of propeller cavitation-induced signal using neural network and wigner-ville distribution

Widjiati, Endang; Djatmiko, Eko Budi; Wardhana, Wisnu; Wirawan, Wirawan

doi:10.1109/oceans-yeosu.2012.6263489

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the late 1980s, many scholars have also applied spectrum analysis to underwater acoustic target feature extraction, such as extracting the line spectrum and modulation spectrum of underwater target radiated noise by using acoustic power spectrum [23], Wigner-Ville distribution (WVD) [24], bispectrum and high-order spectrum analysis [25], empirical mode decomposition (EMD) [26], and wavelet or wavelet packet decomposition [27]. In particular, as a preprocessor, wavelet packet decomposition can divide the frequency space into various finite frequency bands to realize the time-frequency localization of the signal.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Ship Radiation Signals Based on Wavelet Packet Decomposition and Energy Entropy

Ning

Jiang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.

show abstract

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Ship Radiation Signals Based on Wavelet Packet Decomposition and Energy Entropy

Ning

Jiang

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…As for the signal preprocessing method, because of the non-stationarity of cavitation noise, some non-stationary signal analysis methods, such as short-time Fourier transform (STFT) [6], wavelet scalogram [7], wavelet packet [8], Wigner-Ville distribution (WVD) [9], time-domain synchronous averaging (TSA) [10] and empirical mode decomposition (EMD) [11], have been applied to the analysis of cavitation signals. Nevertheless, these methods have some shortcomings: STFT and wavelet methods are limited by basis functions and do not have adaptability; WVD has the problem of cross-term aliasing.…”

Section: Introductionmentioning

confidence: 99%

A Novel Acoustic Method for Cavitation Identification of Propeller

Cui

2022

JMSE

View full text Add to dashboard Cite

When a propeller is under a state of cavitation, it will experience negative effects, including strong noise, vibration, and even damage to the blades. Accordingly, the detection of propeller cavitation has attracted the attention of researchers. Propeller noise signal contains a wealth of cavitation information, which is a powerful method to identify the cavitation state. Considering the nonlinear characteristics of propeller noise, a feature describing the complexity of nonlinear signals, which is called refined composite multiscale fluctuation-based dispersion entropy (RCMFDE), is adopted as the indicator of propeller cavitation, and a framework for the identification of propeller cavitation state is established in this paper. Firstly, the propeller noise signal is decomposed by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, and the intrinsic mode function (IMF) components with cavitation characteristics are extracted. Secondly, the RCMFDE of the IMF components is computed. Finally, a hybrid optimization support vector machine (SVM) is established to classify the features, in which a Relief-F filter is utilized to reduce the feature dimension, and a particle swarm optimization (PSO) wrapper is utilized to optimize the parameters of the SVM. The experimental results demonstrate encouraging accuracy to apply this approach to identify the propeller cavitation states, with an identification accuracy of 91.11%.

show abstract