scholarly journals Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 195
Author(s):  
Qinghua Wang ◽  
Lijuan Wang ◽  
Hongtao Yu ◽  
Dong Wang ◽  
Asoke K. Nandi
Keyword(s):  
Roller Bearing ◽  
Singular Values ◽  
Difference Spectrum ◽  
Rolling Bearings ◽  
Mode Decomposition ◽  
Effective Components ◽  
Stationary Signals ◽  
The Difference ◽  
Band Limited ◽  
Value Decomposition

In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode decomposition (VMD). SVD is used to determine the structure of the underlying model, which is referred to as signal and noise subspaces, and VMD is used to decompose the original signal into several band-limited modes. Then the effective components are selected from these modes to reconstruct the denoised signal according to the difference spectrum (DS) of singular values and kurtosis values. Simulated signals and experimental signals of roller bearing faults have been analyzed using this proposed method and compared with SVD-DS. The results demonstrate that the proposed method can effectively retain the useful signals and denoise the bearing signals in extremely noisy backgrounds.

A Bearing Fault Diagnosis Method Based on VMD-SVD and Fuzzy Clustering

2019 ◽  
Vol 33 (12) ◽  
pp. 1950018 ◽  
Author(s):  
Hongchuan Cheng ◽  
Yimin Zhang ◽  
Wenjia Lu ◽  
Zhou Yang
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Singular Values ◽  
Clustering Methods ◽  
End Effects ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Feature Dimension ◽  
Value Decomposition

To obtain the fault features of the bearing, a method based on variational mode decomposition (VMD), singular value decomposition (SVD) is proposed for fault diagnosis by Gath–Geva (G–G) fuzzy clustering. Firstly, the original signals are decomposed into mode components by VMD accurately and adaptively, and the spatial condition matrix (SCM) can be obtained. The SCM utilized as the reconstruction matrix of SVD can inherit the time delay parameter and embedded dimension automatically, and then the first three singular values from the SCM are used as fault eigenvalues to decrease the feature dimension and improve the computational efficiency. G–G clustering, one of the unsupervised machine learning fuzzy clustering techniques, is employed to obtain the clustering centers and membership matrices under various bearing faults. Finally, Hamming approach degree between the test samples and the known cluster centers is calculated to realize the bearing fault identification. By comparing with EEMD and EMD based on a recursive decomposition algorithm, VMD adopts a novel completely nonrecursive method to avoid mode mixing and end effects. Furthermore, the IMF components calculated from VMD include large amounts of fault information. G–G clustering is not limited by the shapes, sizes and densities in comparison with other clustering methods. VMD and G–G clustering are more suitable for fault diagnosis of the bearing system, and the results of experiment and engineering analysis show that the proposed method can diagnose bearing faults accurately and effectively.

scholarly journals A Novel Wheelset Bearing Fault Diagnosis Method Integrated CEEMDAN, Periodic Segment Matrix, and SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Chenguang Huang ◽  
Jianhui Lin ◽  
Jianming Ding ◽  
Yan Huang
Keyword(s):  
Fault Diagnosis ◽  
Singular Values ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Periodic Impulse ◽  
Value Decomposition

A novel fault diagnosis method, named CPS, is proposed based on the combination of CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise), PSM (periodic segment matrix), and SVD (singular value decomposition). Firstly, the collected vibration signals are decomposed into a set of IMFs using CEEMDAN. Secondly, the PSM of the selected IMFs is constructed. Thirdly, singular values are obtained by SVD conducted on the space of PSM. Fourthly, the impulse components are enhanced by the singular value reconstruction with the first maximal singular value. Finally, the squared envelope spectra of the reconstructed signals are used to diagnose the wheelset bearing faults. The effectiveness of the proposed CPS has been verified by simulations and experiments. Compared to the well-known Hankel-based SVD, the proposed CPS performs better at extracting the weak periodic impulse responses from the measured signals with strong noise and interferences.

scholarly journals Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Songrong Luo ◽  
Junsheng Cheng ◽  
HungLinh Ao
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Values ◽  
Value Decomposition

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

scholarly journals The Fault Feature Extraction of Rolling Bearing Based on EMD and Difference Spectrum of Singular Value

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang ◽  
Nanfei Wang
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
The Difference ◽  
Fault Feature Extraction

Nowadays, the fault diagnosis of rolling bearing in aeroengines is based on the vibration signal measured on casing, instead of bearing block. However, the vibration signal of the bearing is often covered by a series of complex components caused by other structures (rotor, gears). Therefore, when bearings cause failure, it is still not certain that the fault feature can be extracted from the vibration signal on casing. In order to solve this problem, a novel fault feature extraction method for rolling bearing based on empirical mode decomposition (EMD) and the difference spectrum of singular value is proposed in this paper. Firstly, the vibration signal is decomposed by EMD. Next, the difference spectrum of singular value method is applied. The study finds that each peak on the difference spectrum corresponds to each component in the original signal. According to the peaks on the difference spectrum, the component signal of the bearing fault can be reconstructed. To validate the proposed method, the bearing fault data collected on the casing are analyzed. The results indicate that the proposed rolling bearing diagnosis method can accurately extract the fault feature that is submerged in other component signals and noise.

scholarly journals Research on coal-rock recognition based on sound signal analysis

2018 ◽  
Vol 232 ◽  
pp. 04075
Author(s):  
Xihui Chen ◽  
Zenan Yang ◽  
Gang Cheng
Keyword(s):  
Signal Analysis ◽  
Singular Values ◽  
Sound Signal ◽  
Cutting Condition ◽  
Personal Safety ◽  
Signal Features ◽  
Mode Decomposition ◽  
Coal Rock ◽  
Learning Machine ◽  
Value Decomposition

The recognition of the cutting state of shearer is the key technology to realize variable speed cutting and mining automation. It is of great significance for improving shearer reliability, ensuring personal safety and improving coal quality. This paper proposed a coal-rock recognition method based on sound signal analysis. The original sound signal produced during the cutting process of shearer is decomposed by variational mode decomposition (VMD), and the obtained IMFs can construct a signal matrix. The signal matrix is processed by singular value decomposition (SVD), and a series of singular values can be obtained and defined as the signal features. Finally, the coal-rock recognition is realized by extreme learning machine (ELM) based on the extracted signal features. The experiment results show that the overall recognition accuracy is 91.7% under the actual cutting condition, which verifies the effectiveness of the proposed method in coal-rock recognition, and lays a theoretical foundation for the automation and intellectualization of shearer mining.

Minimization of Noise in Spectral Data

1996 ◽  
Vol 50 (6) ◽  
pp. 747-752 ◽  
Author(s):  
Tetsuo Iwata ◽  
Jun Koshoubu
Keyword(s):  
Spectral Data ◽  
Infrared Absorption Spectrum ◽  
Computation Time ◽  
Difference Spectrum ◽  
Previous Method ◽  
Minimization Method ◽  
Data Division ◽  
The Difference ◽  
Value Decomposition ◽  
Advanced Version

We have developed a novel noise-minimization method that is applicable to all kinds of spectral data, which introduces little distortion in the signal waveform. It is based on the singular value decomposition of a matrix and is an advanced version of the conventional method for minimizing the background noise from a line spectrum, the details of which we reported in a previous paper [Appl. Spectrosc. 48, 1453 (1994).]. In order to cope with a continuum spectrum, we have calculated a difference spectrum between an observed spectrum and a smoothed one. After minimizing noise components from the difference spectrum by the use of the previous method, we add it to the smoothed spectrum. We also propose a method for reducing the computation time by introducing a data-division technique. Noise minimization for an infrared absorption spectrum of polystyrene is shown.

scholarly journals Discrimination between Ictal and Seizure-Free EEG Signals Using Empirical Mode Decomposition

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Intracranial Eeg ◽  
Mode Decomposition ◽  
Significant Difference ◽  
Finite Set ◽  
The Difference ◽  
Band Limited ◽  
Electroencephalogram Eeg

A new method for analysis of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion has been presented in this paper. The EMD decomposes an EEG signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB expansion. The MF measure of the IMFs has been used as a feature in order to identify the difference between ictal and seizure-free intracranial EEG signals. It has been shown that the MF feature of the IMFs has provided statistically significant difference between ictal and seizure-free EEG signals. Simulation results are included to illustrate the effectiveness of the proposed method.

scholarly journals Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

2009 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Junsheng Cheng ◽  
Dejie Yu ◽  
Jiashi Tang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

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