scholarly journals Propeller feature extraction of UUVs study based on CEEMD combined with symmetric correlation

2021 ◽  
Vol 336 ◽  
pp. 02006
Author(s):  
Jiangqiao Li ◽  
Li Jiang ◽  
Hongyu Chen ◽  
Ye Zhang ◽  
Yuchao Xie
Keyword(s):  
Noise Suppression ◽  
Ensemble Empirical Mode Decomposition ◽  
Radiated Noise ◽  
Low Snr ◽  
Mode Decomposition ◽  
Correlation Processing ◽  
Radiation Noise ◽  
Manual Selection ◽  
Suppress Noise ◽  
Selection Of

In this paper, in view of the characteristic that UUV radiation noise is low and easily interfered by strong noise, the complementary Ensemble Empirical Mode Decomposition (CEEMD) combined with symmetric correlation processing is proposed, which can improve the extraction performance of UUV’s propeller features. First, the CEEMD decomposition combined with symmetric correlation processing was used to reduce the radiated noise of the target, then the signals after the noise reduction were demodulated and computed to obtain the DEMON spectrum, and finally features such as the rotational speed of the UUV’s propeller were extracted from the DEMON spectrum. The Sea trials signal processing results prove that the method has better noise suppression performance under low SNR conditions, and can clearly and comprehensively extract the DEMON information of the radiated noise, and then accurately extract the propeller features of the UUV. Compared to conventional demodulation techniques, this technique has a greater ability to suppress noise and does not require manual selection of the modulated frequency band.

scholarly journals A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 597 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Measurement Technology ◽  
Signal Denoising ◽  
Frequency Noise ◽  
Denoising Method ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Adaptive Noise

Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen’s chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.

scholarly journals Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S-Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bin Liu ◽  
Youqian Feng ◽  
Zhonghai Yin ◽  
Xiangyu Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Prior Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Mode Decomposition ◽  
S Transform ◽  
Emitter Recognition ◽  
Generalized S Transform

Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

Denoising of the Base Vibration Signal Based on EEMD

2011 ◽  
Vol 143-144 ◽  
pp. 689-693 ◽  
Author(s):  
X.J. Li ◽  
K. Wang ◽  
G.B. Wang ◽  
Q. Li
Keyword(s):  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Decomposition Level ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Common ◽  
Eemd Method ◽  
Selection Of ◽  
Better Than

Vibration signals of rotating machinery on the base are very weak and always buried in noisy noise; the common denoising methods have become powerless. It presents an ensemble empirical mode decomposition method (EEMD) that is used to denoise for the base vibration signal, which not only to overcome the problem of mode mixing, but also to avoid the selection of wavelet basis function and decomposition level of the problem. Experimental results of simulation and measured data show that EEMD method can effectively reduce the base vibration signal noise, which is better than the wavelet and EMD denoising method.

Research on Fault Diagnosis for Gear Based on Ensemble Empirical Mode Decomposition and Slice Bi-Spectrum

2013 ◽  
Vol 718-720 ◽  
pp. 934-939
Author(s):  
Gui Ji Tang ◽  
Xiao Long Wang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Ensemble Empirical Mode Decomposition ◽  
Quadratic Phase ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Signal ◽  
Harmonic Components ◽  
Simulation Signal

A new method on fault diagnosis for gear based on ensemble empirical mode decomposition and slice bi-spectrum is proposed. Firstly, fault signal was decomposed into a series of intrinsic mode function components of different frequency bands by EEMD, and then calculated the envelope signal of IMF component by Hilbert demodulation method. Finally, analyzed the envelope signal by slice bi-spectrum and extracted the fault characteristic frequency. The anti-alias decomposition capacity of EEMD and capabilities of noise suppression and non-quadratic phase coupling harmonic components elimination of slice bi-spectrum were verified by analyzing the simulation signal. The analysis results of gear pitting failure signal and gear wear fault signal showed that this method could judge gear fault type accurately and has a certainly degree reliability.

scholarly journals Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion

2020 ◽  
Vol 10 (11) ◽  
pp. 3790 ◽  
Author(s):  
Jinyong Zhang ◽  
Linlu Dong ◽  
Nuwen Xu
Keyword(s):  
Empirical Mode Decomposition ◽  
Akaike Information Criterion ◽  
Noise Suppression ◽  
Information Criterion ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition

Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (SNRs) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the SNRs of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.

scholarly journals Multi-Fault Diagnosis of Gearbox Based on Improved Multipoint Optimal Minimum Entropy Deconvolution

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 611 ◽  
Author(s):  
Fuhe Yang ◽  
Xingquan Shen ◽  
Zhijian Wang
Keyword(s):  
White Noise ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Selection Of

Under complicated conditions, the extraction of a multi-fault in gearboxes is difficult to achieve. Due to improper selection of methods, leakage diagnosis or misdiagnosis will usually occur. Ensemble Empirical Mode Decomposition (EEMD) often causes energy leakage due to improper selection of white noise during signal decomposition. Considering that only a single fault cycle can be extracted when MOMED (Multipoint Optimal Minimum Entropy Deconvolution) is used, it is necessary to perform the sub-band processing of the compound fault signal. This paper presents an adaptive gearbox multi-fault-feature extraction method based on Improved MOMED (IMOMED). Firstly, EEMD decomposes the signal adaptively and selects the intrinsic mode functions with strong correlation with the original signal to perform FFT (Fast Fourier transform); considering the mode-mixing phenomenon of EEMD, reconstruct the intrinsic mode functions with the same timescale, and obtain several intrinsic mode functions of the same scale to improve the entropy of fault features. There is a lot of white noise in the original signal, and EEMD can improve the signal-to-noise ratio of the original signal. Finally, through the setting of different noise-reduction intervals to extract fault features through MOMED. The proposed method is compared with EEMD and VMD (Variational Mode Decomposition) to verify its feasibility.

scholarly journals A Nonparametric Method for Automatic Denoising of Microseismic Data

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Pingan Peng ◽  
Liguan Wang
Keyword(s):  
Power Spectrum ◽  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Ensemble Empirical Mode Decomposition ◽  
P Wave ◽  
Noise Power ◽  
Microseismic Data ◽  
Mode Decomposition ◽  
Synthetic Datasets ◽  
Microseismic Signal

Noise suppression or signal-to-noise ratio (SNR) enhancement is often desired for better processing results from a microseismic dataset. In this paper, we proposed a nonparametric automatic denoising algorithm for microseismic data. The method consists of three major steps: (1) applying a two-step AIC algorithm to pick P-wave arrival; (2) subtracting the noise power spectrum from the signal power spectrum; (3) recovering the microseismic signal by inverse Fourier transform. The proposed method is tested on synthetic datasets with different signal types and SNRs, as well as field datasets. The results of the proposed method are compared against ensemble empirical mode decomposition (EEMD) and wavelet denoising methods, which shows the effectiveness of the method for denoising and improving the SNR of microseismic data.

scholarly journals Precise Loran-C Signal Acquisition Based on Envelope Delay Correlation Method

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2329 ◽  
Author(s):  
Wenhe Yan ◽  
Kunjuan Zhao ◽  
Shifeng Li ◽  
Xinghui Wang ◽  
Yu Hua
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Method ◽  
Satellite System ◽  
Signal Acquisition ◽  
Test Results ◽  
Low Snr ◽  
Global Navigation Satellite ◽  
Suppress Noise ◽  
Acquisition Method ◽  
Selection Of

The Loran-C system is an internationally standardized positioning, navigation, and timing service system. It is the most important backup and supplement for the global navigation satellite system (GNSS). However, the existing Loran-C signal acquisition methods are easily affected by noise and cross-rate interference (CRI). Therefore, this article proposes an envelope delay correlation acquisition method that, when combined with linear digital averaging (LDA) technology, can effectively suppress noise and CRI. The selection of key parameters and the performance of the acquisition method are analyzed through a simulation. When the signal-to-noise ratio (SNR) is −16 dB, the acquisition probability is more than 90% and the acquisition error is less than 1 μs. When the signal-to-interference ratio (SIR) of the CRI is −5 dB, the CRI can also be suppressed and the acquisition error is less than 5 μs. These results show that our acquisition method is accurate. The performance of the method is also verified by actual signals emitted by a Loran-C system. These test results show that our method can reliably detect Loran-C pulse group signals over distances up to 1500 km, even at low SNR. This will enable the modern Loran-C system to be a more reliable backup for the GNSS system.

scholarly journals Speech Endpoint Detection in Noisy Environment Based on the Ensemble Empirical Mode Decomposition

2011 ◽  
Vol 2-3 ◽  
pp. 135-139
Author(s):  
Jing Jiao Li ◽  
Dong An ◽  
Jiao Wang ◽  
Chao Qun Rong
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Recognition System ◽  
Ensemble Empirical Mode Decomposition ◽  
Endpoint Detection ◽  
Noisy Environment ◽  
Low Snr ◽  
Voice Signal ◽  
Mode Decomposition ◽  
Speech Endpoint Detection

Speech endpoint detection is one of the key problems in the practical application of speech recognition system. In this paper, speech signal contained chirp is decomposed into several intrinsic mode function (IMF) with the method of ensemble empirical mode decomposition (EEMD). At the same time, it eliminates the modal mix superposition phenomenon which usually comes out in processing speech signal with the algorithm of empirical mode decomposition (EMD). After that, selects IMFs contained major noise through the adaptive algorithm. Finally, the IMFs and speech signal contained chirp are input into the independent component analysis (ICA) and pure voice signal is separated out. The accuracy of speech endpoint detection can be improved in this way. The result shows that the new speech endpoint detection method proposed above is effective, and has strong anti-noises ability, especially suitable for the speech endpoint detection in low SNR.

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