scholarly journals High-G Calibration Denoising Method for High-G MEMS Accelerometer Based on EMD and Wavelet Threshold

Micromachines ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 134 ◽  
Author(s):  
Qing Lu ◽  
Lixin Pang ◽  
Haoqian Huang ◽  
Chong Shen ◽  
Huiliang Cao ◽  
...  
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Original Signal ◽  
Denoising Method ◽  
Mems Accelerometer ◽  
Mode Decomposition ◽  
Impact Peak ◽  
Reconstructed Signal ◽  
Mems Accelerometers ◽  
The Impact

High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) and wavelet threshold is proposed. Firstly, EMD decomposition is performed on the output of the main accelerometer to obtain the intrinsic mode function (IMF). Then, the continuous mean square error rule is used to find energy cut-off point, and then the corresponding high frequency IMF component is denoised by wavelet threshold. Finally, the processed high-frequency IMF component is superposed with the low-frequency IMF component, and the reconstructed signal is denoised signal. Experimental results show that this method integrates the advantages of EMD and wavelet threshold and can retain useful signals to the maximum extent. The impact peak and vibration characteristics are 0.003% and 0.135% of the original signal, respectively, and it reduces the noise of the original signal by 96%.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals An Image Denoising Method with Enhancement of the Directional Features Based on Wavelet and SVD Transforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Min Wang ◽  
Zhen Li ◽  
Xiangjun Duan ◽  
Wei Li
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Image Denoising ◽  
High Frequency ◽  
Low Frequency ◽  
Singular Value ◽  
Denoising Method ◽  
Diagonal Part ◽  
Value Decomposition ◽  
Image Part

This paper proposes an image denoising method, using the wavelet transform and the singular value decomposition (SVD), with the enhancement of the directional features. First, use the single-level discrete 2D wavelet transform to decompose the noised image into the low-frequency image part and the high-frequency parts (the horizontal, vertical, and diagonal parts), with the edge extracted and retained to avoid edge loss. Then, use the SVD to filter the noise of the high-frequency parts with image rotations and the enhancement of the directional features: to filter the diagonal part, one needs first to rotate it 45 degrees and rotate it back after filtering. Finally, reconstruct the image from the low-frequency part and the filtered high-frequency parts by the inverse wavelet transform to get the final denoising image. Experiments show the effectiveness of this method, compared with relevant methods.

scholarly journals Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3510 ◽  
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.

scholarly journals Statistical regularities cause attentional suppression with target-matching distractors

2020 ◽  
Author(s):  
Dirk Kerzel ◽  
Stanislas Huynh Cong
Keyword(s):  
High Frequency ◽  
Target Location ◽  
Low Frequency ◽  
Target Display ◽  
Distractor Location ◽  
Target Feature ◽  
Statistical Regularities ◽  
Feature Based ◽  
The Impact ◽  
Target Matching

AbstractVisual search may be disrupted by the presentation of salient, but irrelevant stimuli. To reduce the impact of salient distractors, attention may suppress their processing below baseline level. While there are many studies on the attentional suppression of distractors with features distinct from the target (e.g., a color distractor with a shape target), there is little and inconsistent evidence for attentional suppression with distractors sharing the target feature. In this study, distractor and target were temporally separated in a cue–target paradigm, where the cue was shown briefly before the target display. With target-matching cues, RTs were shorter when the cue appeared at the target location (valid cues) compared with when it appeared at a nontarget location (invalid cues). To induce attentional suppression, we presented the cue more frequently at one out of four possible target positions. We found that invalid cues appearing at the high-frequency cue position produced less interference than invalid cues appearing at a low-frequency cue position. Crucially, target processing was also impaired at the high-frequency cue position, providing strong evidence for attentional suppression of the cued location. Overall, attentional suppression of the frequent distractor location could be established through feature-based attention, suggesting that feature-based attention may guide attentional suppression just as it guides attentional enhancement.

scholarly journals On the contamination of the global 21 cm signal from polarized foregrounds

2019 ◽  
Author(s):  
Marta Spinelli ◽  
Gianni Bernardi ◽  
Mario G Santos
Keyword(s):  
Frequency Band ◽  
Low Frequency ◽  
Complex Frequency ◽  
Gaussian Profile ◽  
Reconstructed Signal ◽  
Linearly Polarized ◽  
Exotic Physics ◽  
Single Polarization ◽  
The Impact ◽  
The Universe

Abstract Global (i.e. sky-averaged) 21 cm signal experiments can measure the evolution of the universe from the Cosmic Dawn to the Epoch of Reionization. These measurements are challenged by the presence of bright foreground emission that can be separated from the cosmological signal if its spectrum is smooth. This assumption fails in the case of single polarization antennas as they measure linearly polarized foreground emission - which is inevitably Faraday rotated through the interstellar medium. We investigate the impact of Galactic polarized foregrounds on the extraction of the global 21 cm signal through realistic sky and dipole simulations both in a low frequency band from 50 to 100 MHz, where a 21 cm absorption profile is expected, and in a higher frequency band (100 − 200 MHz). We find that the presence of a polarized contaminant with complex frequency structure can bias the amplitude and the shape of the reconstructed signal parameters in both bands. We investigate if polarized foregrounds can explain the unexpected 21 cm Cosmic Dawn signal recently reported by the EDGES collaboration. We find that unaccounted polarized foreground contamination can produce an enhanced and distorted 21 cm absorption trough similar to the anomalous profile reported by Bowman et al. (2018), and whose amplitude is in mild tension with the assumed input Gaussian profile (at ∼1.5σ level). Moreover, we note that, under the hypothesis of contamination from polarized foreground, the amplitude of the reconstructed EDGES signal can be overestimated by around 30%, mitigating the requirement for an explanation based on exotic physics.

scholarly journals Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 694 ◽  
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Shuguo Pan ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
High Precision ◽  
High Frequency ◽  
Spectral Response ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Full Time ◽  
Dynamic Displacement ◽  
Mode Decomposition ◽  
Acceleration Sensors

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

scholarly journals The Impact of Physical Environment and Regional Image on Tourist’s Experiential Values and Feelings: An Example of Nanzhuang Area in Taiwan

2015 ◽  
Vol 10 (11) ◽  
pp. 94
Author(s):  
Shwu-Ing Wu ◽  
Li Chia Huang
Keyword(s):  
Significant Influence ◽  
Physical Environment ◽  
High Frequency ◽  
Low Frequency ◽  
Equation Modeling ◽  
Independent Variables ◽  
Frequency Group ◽  
Experiential Value ◽  
The Impact ◽  
Two Independent Variables

With the booming global tourism activities, many countries around the world are actively promoting regional tourism. Thus, understanding the tourists’ needs is important in developing tourism promotion strategies. With Nanzhuang Township, Miaoli County as the case study, this paper discusses the influence of the two independent variables, the tangible physical environment and the intangible regional image, tourists’ experiential value and the feelings after tourism. This study conducted a questionnaire survey on tourists who have visited Nanzhuang Township, Miaoli County, by convenience sampling, in order to construct the model of regional experience marketing effect. A total of 743 effective samples were retrieved. After analysis by structural equation modeling (SEM), it is found that: (1) the physical environment has a positive and significant influence on the tourists’ experiential value; (2) regional image has a positive and significant influence on the tourist’s experiential value; (3) the experiential value has a positive and significant influence on satisfaction; (4) satisfaction has a positive and significant influence on trust and commitment; (5) trust has no significant influence on commitment. Regarding the two independent variables, regional image has more influence. In addition, after comparing the group models by clustering with the high and low frequency of the number of visits, it is found that there are some differences between the high frequency group and the low frequency group, where the regional image of the high frequency group has a greater influence on the experiential value and the physical environment of the low frequency group has a greater influence on the experiential value. The findings can serve as reference for the local government and the tourism operators to develop regional marketing strategies.

scholarly journals Load Prediction Based on Hybrid Model of VMD-mRMR-BPNN-LSSVM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Gang Zhang ◽  
Hongchi Liu ◽  
Pingli Li ◽  
Meng Li ◽  
Qiang He ◽  
...  
Keyword(s):  
Power System ◽  
High Frequency ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Low Frequency ◽  
Load Prediction ◽  
System Load ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Feature Information

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

scholarly journals Denoising of the Fiber Bragg Grating Deformation Spectrum Signal Using Variational Mode Decomposition Combined with Wavelet Thresholding

2019 ◽  
Vol 9 (1) ◽  
pp. 180 ◽  
Author(s):  
Weifang Zhang ◽  
Meng Zhang ◽  
Yan Zhao ◽  
Bo Jin ◽  
Wei Dai
Keyword(s):  
Discrete Wavelet ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Noisy Signals ◽  
Band Limited ◽  
Adaptive Signal Decomposition ◽  
Adaptive Signal ◽  
The Impact

Damage detection using an FBG sensor is a critical process for an assessment of any inspection technology classified as structural health monitoring (SHM). FBG signals containing noise in experiments are developed to detect flaws. In this paper, we propose a novel signal denoising method that combines variational mode decomposition (VMD) and changed thresholding wavelets to denoise experimental and mixed signals. VMD is a recently introduced adaptive signal decomposition algorithm. Compared with traditional empirical mode decomposition (EMD), and it is well founded theoretically and more robust to noise samples. First, input signals were broken down into a given number of K band-limited intrinsic mode functions (BLIMFs) by VMD. For the purpose of avoiding the impact of overbinning or underbinning on VMD denoising, the mixed signals, which were obtained by adding different signal/noise ratio (SNR) noises to the experimental signals, were designed to select the best decomposition number K and data-fidelity constraint parameter α. After that, the realistic experimental signals were processed using four denoising algorithms to evaluate denoising performance. The results show that, upon adding additional noisy signals and realistic signals, the proposed algorithm delivers excellent performance over the EMD-based denoising method and discrete wavelet transform filtering.

Denoising Method for Calibration Data of Landing Guidance Radar Based on EMD and Wavelet

2014 ◽  
Vol 962-965 ◽  
pp. 2856-2862
Author(s):  
De Yi Sang ◽  
Jian Jun Zhao ◽  
Li Bin Yang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Wavelet Denoising ◽  
Calibration Data ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Calibration Process ◽  
Frequency Components

The noise resulted in the calibration process of the landing guidance radar can cause serious accidents. Analyse the principle of the EMD and wavelet denoising method. Points out the deficiencies of pure EMD or pure wavelet denoising method. Propose a denoising method based on EMD and wavelet. Improved the discriminanting method for high or low frequency components and the discriminanting method for wavelet thresholding. First EMD the signal, then denoise the high frequency components by wavelet, finally, combined the low frequency components and the denoised high frequency components to get the denoised data.

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