A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

Abstract

Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first decompose three types of S-RN into sub-signals, named intrinsic mode functions (IMFs). Then, we obtain the features of energy difference and energy entropy based on IMFs, named CEEMDAN-ED and CEEMDAN-EE, respectively. Finally, we compare the recognition rate for three sorts of S-RN by using the following three energy feature extraction approaches, which are CEEMDAN-ED, CEEMDAN-EE and CEEMDAN-ED-EE. The experimental results prove the effectivity and the high recognition rate of the proposed approach.

A Novel Improved Feature Extraction Technique for Ship-Radiated Noise Based on IITD and MDE

“…In order to compare ITD, IITD, and EMD, simulation signals are taken as (16) where, consists of with the sampling frequency of 1 kHz and standard Gaussian white noise .…”

“…Hong [15] proposed ensemble EMD (EEMD) and energy distribution to extract the energy difference, which is an efficient feature extraction technique for ship-radiated noise. Li [16] proposed an improved energy feature extraction technique for ship-radiated noise, which combined 2.1.1. ITD Suppose {X t , t ≥ 0} is a real-valued signal, let {τ k , k = 1, 2, · · ·} denote the local extrema of X t , and for convenience define τ 0 = 0. we defined L as the baseline extraction operator for X t , and X t can be decomposed as [17]:…”

Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification

“…The vibration signal of a ball mill is nonlinear and nonstationary. Currently, the most widely used methods for processing such signals include the wavelet packet algorithm, empirical mode decomposition (EMD), variable mode decomposition (VMD), local mean decomposition (LMD), and the complete integrated empirical decomposition algorithm (CEEMDAN) [11][12][13][14]. Liu et al [15] combined the EMD algorithm with principal component analysis (PCA) to extract the vibration signal from the cylinder of a wet ball mill.…”

Fault Diagnosis for Rail Vehicle Axle-Box Bearings Based on Energy Feature Reconstruction and Composite Multiscale Permutation Entropy

“…In order to overcome the drawbacks of EMD, such as the boundary effect, mode mixing, and under-and over-shoot problems, improved methods, including ensemble EMD (EEMD) [10], complementary EEMD (CEEMD) [11], and complete EEMD with adaptive noise (CEEMDAN) [12] have been proposed. In [13][14][15], the CEEMDAN and VMD methods have been applied for feature extraction and denoising of underwater acoustic signals.EMD and its derivative methods, LMD, and VMD methods can decompose complicated signals self-adaptively; however, the signal frequency bands cannot be accurately divided, and the IMF decomposition results are related to the original signal characteristics, which cannot form a uniform frequency distribution. WPT can decompose a signal with multiple scales and high resolution based on the frequency distribution with more uniform frequency feature extraction results, which is beneficial to the unified feature extraction of different frequency bands of fault-impact signals and facilitates intelligent classification of multiple sets of signals.…”

“…In order to overcome the drawbacks of EMD, such as the boundary effect, mode mixing, and under-and over-shoot problems, improved methods, including ensemble EMD (EEMD) [10], complementary EEMD (CEEMD) [11], and complete EEMD with adaptive noise (CEEMDAN) [12] have been proposed. In [13][14][15], the CEEMDAN and VMD methods have been applied for feature extraction and denoising of underwater acoustic signals.…”