Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.

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MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Sensors ◽

10.3390/s19214622 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4622 ◽

Cited By ~ 1

Author(s):

Huichao Yan ◽

Ting Xu ◽

Peng Wang ◽

Linmei Zhang ◽

Hongping Hu ◽

...

Keyword(s):

Correlation Coefficient ◽

Optimization Algorithm ◽

Fitness Function ◽

Whale Optimization Algorithm ◽

Signal Denoising ◽

Original Signal ◽

Variational Mode Decomposition ◽

Baseline Drift ◽

Mode Decomposition ◽

Whale Optimization

Underwater acoustic technology is an important means of detecting the ocean. Due to the complex influence of the marine environment, there is a lot of noise and baseline drift in the signals collected by hydrophones. In order to solve this problem, this paper proposes a denoising and baseline drift removal algorithm for MEMS vector hydrophone based on whale-optimized variational mode decomposition (VMD) and correlation coefficient (CC). Firstly, the power spectrum entropy (PSE), which reflects the variation characteristics of the signal frequency is selected as the fitness function of the whale-optimization algorithm to find the parameters (K,α) of the VMD. It is easier to find the global optimal solution of the parameters by combining the whale-optimization algorithm. Then, using the VMD algorithm after obtaining the parameters, the original signal is decomposed to obtain the intrinsic mode functions (IMFs), and calculating the correlation coefficients (CCs) between the IMFs and the original signal. Finally, the CC threshold is used to remove the noise IMFs, and the rest of the useful IMFs are reconstructed to complete the denoising and baseline drift removal process of the original signals. In the simulation experiments, the algorithm proposed in this paper shows better performance by comparing conventional digital signal-processing methods and the related algorithms proposed recently. Applied in the experiments of a MEMS hydrophone, the effectiveness of the proposed algorithm is also verified. This algorithm can provide new ideas for signal denoising and baseline drift removal.

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Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽

10.3390/s18103510 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3510 ◽

Cited By ~ 24

Author(s):

Zhijian Wang ◽

Junyuan Wang ◽

Wenhua Du

Keyword(s):

Fault Diagnosis ◽

Signal To Noise Ratio ◽

Optimal Number ◽

Original Signal ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Multiple Fault ◽

Mode Decomposition ◽

Fault Features ◽

Reconstructed Signal

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

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Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211063688 ◽

2021 ◽

pp. 014233122110636

Author(s):

Dongmei Wang ◽

Lijuan Zhu ◽

Jikang Yue ◽

Jingyi Lu ◽

Gongfa Li

Keyword(s):

Search Algorithm ◽

Signal To Noise Ratio ◽

Decomposition Algorithm ◽

Signal Denoising ◽

Two Dimensional ◽

Variational Mode Decomposition ◽

Denoising Method ◽

Mode Decomposition ◽

Penalty Factor ◽

Whale Optimization

To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

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An optimized variational mode extraction method for rolling bearing fault diagnosis

Structural Health Monitoring ◽

10.1177/14759217211006637 ◽

2021 ◽

pp. 147592172110066

Author(s):

Bin Pang ◽

Mojtaba Nazari ◽

Zhenduo Sun ◽

Jiaying Li ◽

Guiji Tang

Keyword(s):

Extraction Method ◽

Fitness Function ◽

Rolling Bearing ◽

Initial Guess ◽

Center Frequency ◽

Variational Mode Decomposition ◽

Bearing Fault ◽

Mode Decomposition ◽

Two Parameters ◽

Mode Extraction

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

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Complex variational mode decomposition for signal processing applications

Mechanical Systems and Signal Processing ◽

10.1016/j.ymssp.2016.09.032 ◽

2017 ◽

Vol 86 ◽

pp. 75-85 ◽

Cited By ~ 47

Author(s):

Yanxue Wang ◽

Fuyun Liu ◽

Zhansi Jiang ◽

Shuilong He ◽

Qiuyun Mo

Keyword(s):

Signal Processing ◽

Variational Mode Decomposition ◽

Mode Decomposition

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Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

Intelligent Systems ◽

10.4018/978-1-5225-5643-5.ch031 ◽

2018 ◽

pp. 771-797

Author(s):

Abdul Kayom Md Khairuzzaman ◽

Saurabh Chaudhury

Keyword(s):

Image Segmentation ◽

Computational Complexity ◽

Signal To Noise Ratio ◽

Fitness Function ◽

Multilevel Thresholding ◽

Bacterial Foraging Optimization ◽

Swarm Optimization ◽

Kapur’S Entropy ◽

Moth Flame Optimization Algorithm ◽

Popular Image

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

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A Novel Approach for Acoustic Signal Processing of a Drum Shearer Based on Improved Variational Mode Decomposition and Cluster Analysis

Sensors ◽

10.3390/s20102949 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2949

Author(s):

Changpeng Li ◽

Tianhao Peng ◽

Yanmin Zhu

Keyword(s):

Signal Processing ◽

Acoustic Signal ◽

Clustering Algorithm ◽

Pso Algorithm ◽

Variational Mode Decomposition ◽

Novel Approach ◽

Mode Decomposition ◽

Drum Shearer ◽

Best Parameter ◽

And Cluster Analysis

During operation, the acoustic signal of the drum shearer contains a wealth of information. The monitoring or diagnosis system based on acoustic signal has obvious advantages. However, the signal is challenging to extract and recognize. Therefore, this paper proposes an approach for acoustic signal processing of a shearer based on the parameter optimized variational mode decomposition (VMD) method and a clustering algorithm. First, the particle swarm optimization (PSO) algorithm searched for the best parameter combination of the VMD. According to the results, the approach determined the number of modes and penalty parameters for VMD. Then the improved VMD algorithm decomposed the acoustic signal. It selected the ideal component through the minimum envelope entropy. The PSO was designed to optimize the clustering analysis, and the minimum envelope entropy of the acoustic signal was regarded as the feature for classification. We then use a shearer simulation platform to collect the acoustic signal and use the approach proposed in this paper to process and classify the signal. The experimental results show that the approach proposed can effectively extract the features of the acoustic signal of the shearer. The recognition accuracy of the acoustic signal was high, which has practical application value.

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Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Applied Sciences ◽

10.3390/app10062146 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2146 ◽

Cited By ~ 1

Author(s):

Jingxuan Zhang ◽

Hexu Sun ◽

Zexian Sun ◽

Yan Dong ◽

Weichao Dong

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Power Converter ◽

Open Circuit ◽

Original Signal ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Igbt Modules ◽

Igbt Module ◽

Circuit Fault Diagnosis

The power converter is a significant device in a wind power system. The wind turbine will be shut down and off grid immediately with the occurrence of the insulated gate bipolar transistor (IGBT) module open-circuit fault of the power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies for the power converter there are few single and double IGBT module open-circuit fault diagnosis methods producing negative results, including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed: Primarily, by collecting the three-phase current varying with a wind speed of 22 states, including a normal state and 21 failure states of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are utilized as the input of the deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

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

Electronics ◽

10.3390/electronics8060597 ◽

2019 ◽

Vol 8 (6) ◽

pp. 597 ◽

Cited By ~ 4

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.

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