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.

Order-statistic filtering Fourier decomposition and its application to rolling bearing fault diagnosis journal of vibration and control

2021 ◽  
pp. 107754632199759
Author(s):  
Siqi Huang ◽  
Jinde Zheng ◽  
Haiyang Pan ◽  
Jinyu Tong
Keyword(s):  
Wavelet Transform ◽  
Order Statistic ◽  
Decomposition Method ◽  
Fourier Spectrum ◽  
Nonstationary Signal ◽  
Transform Method ◽  
Fourier Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform

Inspired by the empirical wavelet transform method, a newly nonstationary signal analysis method–termed order-statistic filtering Fourier decomposition is proposed in this article. First, order-statistic filtering Fourier decomposition uses order-statistic filtering and smoothing to preprocess the Fourier spectrum of original signal, which resolves the problem of unreasonable boundaries obtained by empirical wavelet transform in 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 method. The order-statistic filtering Fourier decomposition method is compared with the existing nonstationary signal decomposition methods including empirical mode decomposition, empirical wavelet transform, FDM, and variational mode decomposition through analyzing simulation signals, and the result indicates that order-statistic filtering Fourier decomposition is much more accurate and reasonable in obtaining mono-components. After that, the order-statistic filtering Fourier decomposition method is compared with the mentioned methods in diagnostic accuracy through analyzing the tested faulty bearing vibration signals and the effectiveness of order-statistic filtering Fourier decomposition to the comparative methods in bearing fault identification are verified.

scholarly journals A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Zechao Liu ◽  
Jianming Ding ◽  
Jianhui Lin ◽  
Yan Huang
Keyword(s):  
Wavelet Transform ◽  
Rolling Bearing ◽  
Scale Space ◽  
Ensemble Empirical Mode Decomposition ◽  
Space Representation ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Engineering Practices ◽  
Fault Information

Rolling element bearings have been widely used in mechanical systems, such as electric motors, generators, pumps, gearboxes, railway axles, and turbines, etc. Therefore, the detection of rolling bearing faults has been a hot research topic in engineering practices. Envelope demodulation represents a fundamental method for extracting effective fault information from measured vibration signals. However, the performance of envelope demodulation depends heavily on the selection of the filter band and central frequencies. The empirical wavelet transform (EWT), a new signal decomposition method, provides a framework for arbitrarily segmenting the Fourier spectrum of an analysed signal. Scale-space representation (SSR) can adaptively detect the boundaries of the EWT; however, it has two shortcomings: slow calculation speeds and invalid boundary detection results. Accordingly, an EWT method based on optimized scale-space representation (OSSR), namely, the EWTOSSR, is proposed. The effectiveness of the EWTOSSR is verified by comparisons between the simulation and the experimental signals. The results show that the EWTOSSR can automatically and effectively segment the EWT spectrum to extract fault information. Compared with three well-known methods (the traditional EWT, ensemble empirical mode decomposition (EEMD), and the fast kurtogram), the EWTOSSR exhibits a much better fault detection performance.

scholarly journals Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang
Keyword(s):  
Random Forest ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ar Model ◽  
Model Parameters ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Non Gaussian

Targeting the nonstationary and non-Gaussian characteristics of vibration signal from fault rolling bearing, this paper proposes a fault feature extraction method based on variational mode decomposition (VMD) and autoregressive (AR) model parameters. Firstly, VMD is applied to decompose vibration signals and a series of stationary component signals can be obtained. Secondly, AR model is established for each component mode. Thirdly, the parameters and remnant of AR model served as fault characteristic vectors. Finally, a novel random forest (RF) classifier is put forward for pattern recognition in the field of rolling bearing fault diagnosis. The validity and superiority of proposed method are verified by an experimental dataset. Analysis results show that this method based on VMD-AR model can extract fault features accurately and RF classifier has been proved to outperform comparative classifiers.

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