scholarly journals Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3536 ◽  
Author(s):  
Binqiang Chen ◽  
Qixin Lan ◽  
Yang Li ◽  
Shiqiang Zhuang ◽  
Xincheng Cao
Keyword(s):  
Feature Extraction ◽  
Wavelet Analysis ◽  
Low Frequency ◽  
Wavelet Packets ◽  
Discrete Wavelet ◽  
Sinusoidal Wave ◽  
Displacement Signal ◽  
Fault Features ◽  
Reconstructed Signal ◽  
Weak Fault

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features.

scholarly journals Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas–Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries

Polymers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3647
Author(s):  
Siavash Hosseini ◽  
Osman Taylan ◽  
Mona Abusurrah ◽  
Thangarajah Akilan ◽  
Ehsan Nazemi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Flow Regime ◽  
Void Fraction ◽  
Low Frequency ◽  
Flow Regimes ◽  
Discrete Wavelet ◽  
Two Phase ◽  
Flow Meters ◽  
Petrochemical Industries

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

scholarly journals Wavelet Transform for IoT – A Signal Processing Perspective

2021 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Low Frequency ◽  
Wavelet Packet Transform ◽  
Discrete Wavelet ◽  
Energy Correlation ◽  
Reconstructed Signal ◽  
Generalized Framework ◽  
Frequency Components ◽  
Initial Results

<div>Denoising of signals in an Internet-of-Things (IoT) network is critically challenging owing to the diverse nature of the nodes generating them, environments through which they travel, characteristics of noise plaguing the signals and the applications they cater to. In order to address the abovementioned challenges, we conceptualize a generalized framework combining wavelet packet transform (WPT) and energy correlation analysis. WPT decomposes both the low-frequency and high-frequency components of the received signals in different time scales and wavelet spaces. Noise components are identified, removed through filtering and the signal components are predicted back after filtering using inverse wavelet packet transform (IWPT). Next energy of the reconstructed signal components are compared with that of the original transmitted signal to modify the characteristics of the decomposed signal components. Using the modified details, the signal components are reconstructed back again and the noise components are filtered out. This process is repeated until noise is completely removed. Initial results suggest that, our proposed framework offers improvement in error probability performance of a medium-scale IoT network over traditional discrete wavelet transform (DWT) and WPT based techniques by around 3 dB and 7 dB respectively.</div>

scholarly journals Weak fault feature extraction of gear based on KVMD and singular value difference spectrum

2018 ◽  
Vol 211 ◽  
pp. 08001
Author(s):  
Hongkun Li ◽  
Chaoge Wang ◽  
Mengfan Hou ◽  
Rui Yang ◽  
Daolong Tang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Difference Spectrum ◽  
Singular Value ◽  
Intrinsic Mode Functions ◽  
Correct Judgment ◽  
Reconstructed Signal ◽  
Gear Fault ◽  
Weak Fault ◽  
Fault Feature Extraction

Gearbox is an important component of many industrial applications. When the gear fault occurs, the vibration signal is characterized by multi-component, multi-frequency modulation, low signal to noise ratio, weak fault characteristics and difficult to extract. This paper proposes a gear fault feature extraction method based on improved variational mode decomposition(VMD) and singular value difference spectrum. Firstly, the method is optimized for the decomposition level K of the VMD algorithm, and an improved method of VMD decomposition layer number K for central frequency screening (KVMD) is proposed. Then, the gear fault vibration signal is decomposed into a series of bandlimited intrinsic mode functions using KVMD. Due to the interference of the noise, it is difficult to make the correct judgment of fault in the spectrum of each mode component. According to the correlation coefficient criterion, the components with larger correlation coefficients are chosen to singular value decomposition. The singular value difference spectrum is obtained, and the effective order of the reconstructed signal is determined from the difference spectrum to denoise the signal; Finally, the processed signal is analyzed by Hilbert envelope. The fault characteristic frequency can be extracted accurately from the envelope spectrum. Through the analysis of the experimental data of gear fault, the results show that the method can effectively reduce the influence of the noise, and accurately realize the extraction of gear fault feature information.

scholarly journals EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy

2019 ◽  
Vol 9 (8) ◽  
pp. 201 ◽  
Author(s):  
Ji ◽  
Ma ◽  
Dong ◽  
Zhang
Keyword(s):  
Feature Extraction ◽  
Signal Reconstruction ◽  
Recognition Rate ◽  
Approximate Entropy ◽  
Stationary Time Series ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
Reconstructed Signal

The classification recognition rate of motor imagery is a key factor to improve the performance of brain–computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support vector machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging,

scholarly journals Wavelet Analysis of Stocks in the Nigerian Capital Market

2019 ◽  
Vol 1 ◽  
pp. 176-183
Author(s):  
C U Okonkwo ◽  
B O Osu ◽  
K Uchendu ◽  
C Chibuisi
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Capital Market ◽  
Banking Sector ◽  
Low Frequency ◽  
Wavelet Spectrum ◽  
Discrete Wavelet ◽  
Low Frequencies ◽  
Beverage Industry ◽  
Food And Beverage

In this paper, the relationship between some selected stocks in the Nigerian Capital Market was investigated using wavelet analysis. The selected stocks are Dangote Cement (Dans) representing the housing sector, Julius Berger (Jbger) representing the Construction industry, Nestle Nigerian Plc (Nese) representing the food and beverages sector and United Bank of Africa (Ubas) representing the banking sector. The goal is to find out how the different sector relate to each other, t also serve as a guide for investors in the Nigerian stock Market. The result shows that on low frequencies, the coherence between stocks are low but volatile. The cross wavelet showed that there is little or no covariability in the magnitude of the movement among the selected stocks but there are co-variability in the direction of the movement. The continuous wavelet transform (wavelet spectrum) shows that for all the stocks, there is high volatility at the low frequency scales and low volatility at high frequency scales. The discrete wavelet transform (DWT) suggests that in the absence of noise, Nese (representing the food and beverage industry is the most volatile stock.

An Enhanced CICA Method and Its Application to Multistage Gearbox Low-frequency Fault Feature Extraction

2020 ◽  
Vol 13 (2) ◽  
pp. 285-294
Author(s):  
Junfa Leng ◽  
Penghui Shi ◽  
Shuangxi Jing ◽  
Chenxu Luo
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Signals ◽  
Gear Fault ◽  
Weak Fault ◽  
Constrained Independent Component Analysis ◽  
Ica Algorithm

Background: The vibration signals acquired from multistage gearbox’s slow-speed gear with localized fault may be directly mixed with source noise and measured noise. In addition, Constrained Independent Component Analysis (CICA) method has strong immunity to the measured noise but not to the source noise. These questions cause the difficulty for applying CICA method to directly extract lowfrequency and weak fault characteristic from the gear vibration signals with source noise. Methods: In order to extract the low-frequency and weak fault feature from the multistage gearbox, the source noise and measured noise are introduced into the independent component analysis (ICA) algorithm model, and then an enhanced Constrained Independent Component Analysis (CICA) method is proposed. The proposed method is implemented by combining the traditional Wavelet Transform (WT) with Constrained Independent Component Analysis (CICA). Results: In this method, the role of a supplementary step of WT before CICA analysis is explored to effectively reduce the influence of strong noise. Conclusion: Through the simulations and experiments, the results show that the proposed method can effectively decrease noise and enhance feature extraction effect of CICA method, and extract the desired gear fault feature, especially the low-frequency and weak fault feature.

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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