scholarly journals Research on Fault Feature Extraction Method Based on FDM-RobustICA and MOMEDA

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Jingzong Yang ◽  
Xuefeng Li ◽  
Limei Wu
Keyword(s):  
Single Channel ◽  
Selection Criterion ◽  
Rolling Bearing ◽  
Minimum Entropy ◽  
Fourier Decomposition ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Useful Signal ◽  
Envelope Spectrum ◽  
Signal Channel

Aiming at the difficulty of extracting rolling bearing fault features under strong background noise conditions, a method based on the Fourier decomposition method (FDM), robust independent component analysis (RobustICA), and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, the FDM method is introduced to decompose the single-channel bearing fault signal into several Fourier intrinsic band functions (FIBF). Secondly, by setting the cross-correlation coefficient and kurtosis as a new selection criterion, it can effectively construct the virtual noise channel and the observation signal channel, which makes RobustICA complete the separation of the useful signal and noise well. Finally, MOMEDA is introduced to enhance the periodic impact components in the denoised signal, and then the filtered signal is analyzed by the Hilbert envelope spectrum to extract the fault characteristic frequency. Through the experimental analysis of the simulated signals and the actual bearing fault signals, the results show that the proposed method not only has the ability to suppress noise and accurately extract fault feature information but also has better performance than the traditional method of local mean decomposition (LMD) and intrinsic time-scale decomposition (ITD), highlighting its practicality in the fault diagnosis of rotating machinery.

Feature extraction for rolling bearing fault diagnosis by electrostatic monitoring sensors

2014 ◽  
Vol 229 (10) ◽  
pp. 1887-1903 ◽  
Author(s):  
Ying Zhang ◽  
Hongfu Zuo ◽  
Fang Bai
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Hankel Matrix ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Quantitative Indicator ◽  
Bearing Fault Diagnosis

There are mainly two problems with the current feature extraction methods used in the electrostatic monitoring of rolling bearings, which affect their abilities to identify early faults: (1) since noises are mixed in the electrostatic signals, it is difficult to extract weak early fault features; (2) traditional time and frequency domain features have limited ability to provide a quantitative indicator of degradation state. With regard to these two problems, a new feature extraction method for rolling bearing fault diagnosis by electrostatic monitoring sensors is proposed in this paper. First, the spectrum interpolation is adopted to suppress the power-frequency interference in the electrostatic signal. Then the resultant signal is used to construct Hankel matrix, the number of useful components is automatically selected based on the difference spectrum of singular values, after that the signal is reconstructed to remove background noises and random pulses. Finally, the permutation entropy of the denoised signal is calculated and smoothed using the exponential weighted moving average method, which is used to be a quantitative indicator of bearing performance state. The simulation and experimental results show that the proposed method can effectively remove noises and significantly bring forward the time when early faults are detected.

Order-statistic filtering Fourier decomposition and its applications to rolling bearing fault diagnosis

2020 ◽  
Author(s):  
Siqi Huang ◽  
Jinde Zheng ◽  
Haiyang Pan ◽  
Jinyu Tong
Keyword(s):  
Order Statistic ◽  
Fourier Spectrum ◽  
Rolling Bearing ◽  
Decomposition Methods ◽  
Nonstationary Signal ◽  
Fourier Decomposition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform

Abstract Inspired by the empirical wavelet transform (EWT) method, a new method for nonstationary signal analysis termed order-statistic filtering Fourier decomposition (OSFFD) is proposed in this paper. The OSFFD method uses order-statistic filtering and smoothing to preprocesses the Fourier spectrum of original signal, which improves the problem of sometimes unreasonable boundaries obtained by EWT directly segmenting the Fourier spectrum. Then, the mono-components with physical significance are obtained by adaptively reconstructing the coefficient of fast Fourier transform in each interval, which improves the problem of too many false components obtained by Fourier decomposition (FDM). The OSFFD method also is compared with the existing nonstationary signal decomposition methods including empirical mode decomposition（EMD）, EWT, FDM and variational mode decomposition（VMD） through analyzing simulation signals and the result indicates that OSFFD is less affected by noise and is much more accurate and reasonable in obtaining mono-components. After that, the OSFFD method is compared with the mentioned methods in diagnostic accuracy through analyzing the tested faulty bearing vibration signals and the effectiveness and superiority of OSFFD to the comparative methods in bearing fault identification are verified.

Bearing Fault Detection Based on Multi-Scale Laplace Wavelet Transform Spectrum

2013 ◽  
Vol 739 ◽  
pp. 413-417
Author(s):  
Ya Ning Wang
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Three Dimensions ◽  
Noise Condition ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Integrated Technique ◽  
Envelope Spectrum

Laplace wavelet transform is self-adaptive to non-stationary and non-linear signal, which can detect the singularity characteristic of a signal precisely under strong background noise condition. A new method of bearing fault diagnosis based on multi-scale Laplace wavelet transform spectrum is proposed. The multi scale Laplace wavelet transform spectrum technique combines the advantages of Laplace wavelet transform, envelope spectrum and three dimensions color map into one integrated technique. The bearing fault vibration signal is firstly decomposed using Laplace wavelet transform. In the end, the multi scale Laplace wavelet transform spectrum is obtained and the characteristics of the bearing fault can be recognized according to the multi-scale Laplace wavelet transform spectrum. The proposed method has been verified by vibration signals obtained from rolling bearing with inner race fault.

Rolling Bearing Fault Diagnosis Based on Second Generation Wavelet Denoising and Improved EEMD

2014 ◽  
Vol 556-562 ◽  
pp. 2677-2680 ◽  
Author(s):  
Ling Jie Meng ◽  
Jia Wei Xiang
Keyword(s):  
Fault Diagnosis ◽  
Second Generation ◽  
Selection Criterion ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Wavelet Denoising ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Second Generation Wavelet

A new rolling bearing fault diagnosis approach is proposed. The original vibration signal is purified using the second generation wavelet denoising. The purified signal is further decomposed by an improved ensemble empirical mode decomposition (EEMD) method. A new selection criterion, including correlation analysis and the first two intrinsic mode functions (IMFs) with the maximum energy, is put forward to eliminate the pseudo low-frequency components. Experimental investigation show that the rolling bearing fault features can be effectively extracted.

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.

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