scholarly journals A Novel Median-Point Mode Decomposition Algorithm for Motor Rolling Bearing Fault Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ganzhou Yao ◽  
Bishuang Fan ◽  
Wen Wang ◽  
Haihang Ma
Keyword(s):  
Extreme Points ◽  
Rolling Bearing ◽  
Decomposition Methods ◽  
Time Interval ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Recognition ◽  
Comparison Results ◽  
Median Point

Precise fault recognition of motor rolling bearing fault is playing a significant role in any machinery and equipment. However, conventional decomposition methods fail to completely reveal the fault signal information of motor rolling bearing due to mixed modes problem. To solve the problem, the median-point mode decomposition (MMD) method is presented. The MMD method uses sort-based inversion to sort out each variation of the same time interval for better and specific mode decomposition, with the assistance of the advanced envelope curve formed by the median points between adjacent extreme points. It certainly alleviates the mixed mode during the iteration of intrinsic mode functions (IMFs). Therefore, comparison results are simulated in the proposed MMD method with conventional methods. Experiment of motor rolling bearing fault is operated for fault recognition in order to demonstrate the MMD algorithm.

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.

scholarly journals GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1946 ◽  
Author(s):  
Jiakai Ding ◽  
Liangpei Huang ◽  
Dongming Xiao ◽  
Xuejun Li
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Optimal Parameter ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Genetic Mutation ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Fault Feature Extraction

The vibration signal of an early rolling bearing is nonstationary and nonlinear, and the fault signal is weak and difficult to extract. To address this problem, this paper proposes a genetic mutation particle swarm optimization variational mode decomposition (GMPSO-VMD) algorithm and applies it to rolling bearing vibration signal fault feature extraction. Firstly, the minimum envelope entropy is used as the objective function of the GMPSO to find the optimal parameter combination of the VMD algorithm. Then, the optimized VMD algorithm is used to decompose the vibration signal of the rolling bearing and several intrinsic mode functions (IMFs) are obtained. The envelope spectrum analysis of GMPSO-VMD decomposed rolling bearing fault signal IMF1 was carried out. Moreover, the feature frequency of the four fault states of the rolling bearing are extracted accurately. Finally, the GMPSO-VMD algorithm is utilized to analyze the simulation signal and rolling bearing fault vibration signal. The effectiveness of the GMPSO-VMD algorithm is verified by comparing it with the fixed parameter VMD (FP-VMD) algorithm, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithm and empirical mode decomposition (EMD) algorithm.

Research on Rolling Element Bearing Fault Diagnosis Based on EEMD and Correlated Kurtosis

2014 ◽  
Vol 680 ◽  
pp. 198-205 ◽  
Author(s):  
Xiao Lin Wang ◽  
Wei Hua Han ◽  
Han Gu ◽  
Cun Hu ◽  
Xing Xing Han
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition

In order to extract the faint fault information from complicated vibration signal of bearing, the correlated kurtosis is introduced into the field of rolling bearing fault diagnosis. Combined with ensemble empirical mode decomposition (EEMD) and correlated kurtosis, a feature extraction method is proposed. According to the method, by EEMD processing a group of intrinsic mode functions (IMFs) are obtained, then the IMF with maximal correlated kurtosis is selected, and the weak fault signal is clearly extracted. The effectiveness of the method is demonstrated on both simulated signal and actual data.

scholarly journals Kurtosis Based Empirical Mode Decomposition for Rolling Bearing Fault Detection

2020 ◽  
Vol 327 ◽  
pp. 03003
Author(s):  
Hui Li ◽  
Xuhan Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Defects ◽  
Mode Decomposition ◽  
Kurtosis Parameter ◽  
Envelope Spectrum

A bearing fault diagnosis approach based on spectral kurtosis and empirical mode decomposition (EMD) is proposed. EMD is a signal decomposition technique, which can adaptively separate a number of intrinsic mode functions (IMFs) from the vibration signal according to the architectural characteristics of the data. The spectral kurtosis parameter takes as signal impulsive indicator. Firstly, EMD is utilized to process the sampling vibration signal. And then spectral kurtosis is calculated to select the optimal intrinsic mode functions, so as to suppress the noise and highlight the transient impact feature. Finally, the envelope spectrum is computed and the fault characteristic is recognized. The experimental results show that the proposed approach can identify bearing defects effectively and provide a reliable method for gearbox fault monitoring and diagnosis.

An optimized variational mode extraction method for rolling bearing fault diagnosis

2021 ◽  
pp. 147592172110066
Author(s):  
Bin Pang ◽  
Mojtaba Nazari ◽  
Zhenduo Sun ◽  
Jiaying Li ◽  
Guiji Tang
Keyword(s):  
Extraction Method ◽  
Fitness Function ◽  
Rolling Bearing ◽  
Initial Guess ◽  
Center Frequency ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Extraction

The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

WHAT ABOUT THE ASYMPTOTIC BEHAVIOR OF THE INTRINSIC MODE FUNCTIONS AS THE SIFTING TIMES TEND TO INFINITY?

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250008 ◽  
Author(s):  
JIN-LIANG WANG ◽  
ZONG-JUN LI
Keyword(s):  
Asymptotic Behavior ◽  
Theoretical Result ◽  
Frequency Distribution ◽  
Extreme Points ◽  
Maximum Amplitude ◽  
Original Data ◽  
Successful Implementation ◽  
Intrinsic Mode Functions ◽  
Interesting Phenomenon ◽  
Mode Decomposition

This paper mainly focuses on discussing the following three suspensions about the intrinsic mode functions (IMFs): (1) Whether more sifting times lead better symmetry to the IMFs or not? (2) Whether the upper and lower envelopes of each IMF degenerate to a pair of symmetric straight lines as the sifting times tend to infinity or not? (3) Whether the average frequencies of the neighboring IMFs get closer enough as the sifting times tend to infinity or not? The first suspension has direct relation to the stoppage criteria, which is of critical importance in the successful implementation of empirical mode decomposition (EMD) method. The second suspension is actually a rigid theoretical result given by Wang et al. [2010], but it may be failed for the actual sifting. The third suspension is associated with the conjecture given by Wu and Huang [2010]. In addition, the frequency distribution problem is also on the way of the last suspension. By considering the variation of maximum amplitude for the mean curve, we find that, after a certain sifting times, the symmetry degree of the IMFs behaves in an intermittent manner. This asymptotic behavior indicates that the first suspension is false in all probability. Further investigation shows that the theoretical result given by Wang et al. [2010] is out of reach. It follows from the sifting tests that the frequency ratio for neighboring IMFs cannot decrease to 1 and the last suspension is cracked. Different from the common sense, there is a very interesting phenomenon that the extreme points of the first IMF may be more than that of the original data. The statistical results on a large quantity of field data show that the frequency distribution along the empirical modes roughly obeys an exponential law. In addition, a new mode-number formula is deduced, which is more reasonable than the one given by Wu and Huang [2009].

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.

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