Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

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Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Sensors ◽

10.3390/s22010112 ◽

2021 ◽

Vol 22 (1) ◽

pp. 112

Author(s):

Hamada Esmaiel ◽

Dongri Xie ◽

Zeyad A. H. Qasem ◽

Haixin Sun ◽

Jie Qi ◽

...

Keyword(s):

Feature Extraction ◽

Recognition Rate ◽

Extraction Methods ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Radiated Noise ◽

Passive Sonar ◽

Multi Stage ◽

Mode Decomposition

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

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Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽

10.3390/e22040468 ◽

2020 ◽

Vol 22 (4) ◽

pp. 468 ◽

Cited By ~ 1

Author(s):

Dongri Xie ◽

Hamada Esmaiel ◽

Haixin Sun ◽

Jie Qi ◽

Zeyad A. H. Qasem

Keyword(s):

Feature Extraction ◽

Correlation Coefficient ◽

Extraction Method ◽

Recognition Rate ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Feature Extraction Method ◽

Radiated Noise ◽

Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise

Entropy ◽

10.3390/e22060620 ◽

2020 ◽

Vol 22 (6) ◽

pp. 620

Author(s):

Dongri Xie ◽

Haixin Sun ◽

Jie Qi

Keyword(s):

Feature Extraction ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Underwater Acoustic ◽

Radiated Noise ◽

Mode Decomposition ◽

Information Coefficient ◽

Maximal Information Coefficient

Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.

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The Data-Driven Optimization Method and Its Application in Feature Extraction of Ship-Radiated Noise with Sample Entropy

Energies ◽

10.3390/en12030359 ◽

2019 ◽

Vol 12 (3) ◽

pp. 359 ◽

Cited By ~ 14

Author(s):

Yuxing Li ◽

Xiao Chen ◽

Jing Yu ◽

Xiaohui Yang ◽

Huijun Yang

Keyword(s):

Feature Extraction ◽

Optimization Method ◽

Sample Entropy ◽

Maximum Energy ◽

Data Driven ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Radiated Noise ◽

Mode Decomposition

The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.

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Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

10.20855/ijav.2019.24.21325 ◽

2019 ◽

Vol 24 (2) ◽

pp. 303-311 ◽

Cited By ~ 5

Author(s):

Xiaoxia Zheng ◽

Guowang Zhou ◽

Dongdong Li ◽

Haohan Ren

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Mode Decomposition ◽

Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

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Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

10.20944/preprints201611.0052.v1 ◽

2016 ◽

Author(s):

Yu-Xing Li ◽

Ya-An Li ◽

Zhe Chen ◽

Xiao Chen

Keyword(s):

Feature Extraction ◽

Empirical Mode Decomposition ◽

Characteristic Parameter ◽

Intrinsic Mode Function ◽

Permutation Entropy ◽

Intrinsic Mode Functions ◽

Characteristic Parameters ◽

Radiated Noise ◽

Mode Function ◽

Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

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A Novel Feature Extraction Method for Ship-Radiated Noise Based on Variational Mode Decomposition and Multi-Scale Permutation Entropy

Entropy ◽

10.3390/e19070342 ◽

2017 ◽

Vol 19 (7) ◽

pp. 342 ◽

Cited By ~ 29

Author(s):

◽

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Permutation Entropy ◽

Variational Mode Decomposition ◽

Feature Extraction Method ◽

Radiated Noise ◽

Multi Scale ◽

Mode Decomposition

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A New Ship-Radiated Noise Feature Extraction Technique Based on Variational Mode Decomposition and Fluctuation-Based Dispersion Entropy

Entropy ◽

10.3390/e21030235 ◽

2019 ◽

Vol 21 (3) ◽

pp. 235 ◽

Cited By ~ 5

Author(s):

Hong Yang ◽

Ke Zhao ◽

Guohui Li

Keyword(s):

Feature Extraction ◽

Feature Vector ◽

Original Signal ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Feature Extraction Method ◽

Radiated Noise ◽

Mode Decomposition ◽

Underwater Acoustic Channels ◽

The Difference

Sea environment complexity and underwater acoustic channels make it hard to extract features of ship-radiated noise signals. This paper presents a novel feature extraction method using the advantages of variational mode decomposition (VMD), fluctuation-based dispersion entropy (FDE) and self-organizing feature map (SOM). Firstly, VMD decomposition of the original signal is used to get a group of bandwidth-limited intrinsic mode functions (IMFs). Then, the difference between the FDE of each IMF and the original signal is calculated, respectively; the IMF with the smallest difference (SIMF) is selected to calculate the FDE as the feature vector. Finally, the characteristic vectors are sent to the SOM classifier to categorize the original signal. The proposed method is applied to feature extraction of real ship-radiated noise signals. The results show that this method is more precise for ship-radiated noise signals feature extraction.

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Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy

Entropy ◽

10.3390/e21020176 ◽

2019 ◽

Vol 21 (2) ◽

pp. 176 ◽

Cited By ~ 4

Author(s):

Guohui Li ◽

Zhichao Yang ◽

Hong Yang

Keyword(s):

Time Series ◽

Feature Extraction ◽

Mutual Information ◽

Empirical Mode Decomposition ◽

Recognition Rate ◽

Support Vector ◽

Svm Classifier ◽

Radiated Noise ◽

Mode Decomposition ◽

Mixing Problem

To improve the recognition accuracy of ship-radiated noise, a feature extraction method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD), mutual information (MI), and differential symbolic entropy (DSE) is proposed in this paper. RPSEMD is an improved empirical mode decomposition (EMD) that alleviates the mode mixing problem of EMD. DSE is a new tool to quantify the complexity of nonlinear time series. It not only has high computational efficiency, but also can measure the nonlinear complexity of short time series. Firstly, the ship-radiated noise is decomposed into a series of intrinsic mode functions (IMFs) by RPSEMD, and the DSE of each IMF is calculated. Then, the MI between each IMF and the original signal is calculated; the sum of MIs is taken as the denominator; and each normalized MI (norMI) is obtained. Finally, each norMI is used as the weight coefficient to weight the corresponding DSE, and the weighted DSE (WDSE) is obtained. The WDSEs are sent into the support vector machine (SVM) classifier to classify and recognize three types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 98.3333%. Consequently, the proposed WDSE method can effectively achieve the classification of ships.

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Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

Shock and Vibration ◽

10.1155/2018/1063645 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Fan Jiang ◽

Zhencai Zhu ◽

Wei Li ◽

Bo Wu ◽

Zhe Tong ◽

...

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Optimal Number ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Time Frequency ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

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