scholarly journals Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Selection Of

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

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 An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

2014 ◽  
Vol 6 ◽  
pp. 676205 ◽  
Author(s):  
Meijiao Li ◽  
Huaqing Wang ◽  
Gang Tang ◽  
Hongfang Yuan ◽  
Yang Yang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Bearing Faults ◽  
Outer Race ◽  
Mode Decomposition ◽  
Acceleration Sensors

In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

Rolling Bearing Localized Defect Evaluation by Multiscale Signature via Empirical Mode Decomposition

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Qingbo He ◽  
Peng Li ◽  
Fanrang Kong
Keyword(s):  
Health Information ◽  
Empirical Mode Decomposition ◽  
Multiple Scales ◽  
Regression Line ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Rolling Element

Measured vibration signals from rolling element bearings with defects are generally nonstationary, and are multiscale in nature owing to contributions from events with different localization in time and frequency. This paper presents a novel approach to characterize the multiscale signature via empirical mode decomposition (EMD) for rolling bearing localized defect evaluation. Vibration signal measured from a rolling element bearing is first adaptively decomposed by the EMD to achieve a series of usable intrinsic mode functions (IMFs) carrying the bearing health information at multiple scales. Then the localized defect-induced IMF is selected from all the IMFs based on a variance regression approach. The multiscale signature, called multiscale slope feature, is finally estimated from the regression line fitted over logarithmic variances of the IMFs excluding the defect IMF. The presented feature reveals the pattern of energy transfer among multiple scales due to localized defects, representing an inherent self-similar signature of the bearing health information that is embedded on multiple analyzed scales. Experimental results verify the performance of the proposed multiscale feature, and further discussions are provided.

scholarly journals A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 995 ◽  
Author(s):  
Tao Liang ◽  
Hao Lu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Mode Decomposition

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peiming Shi ◽  
Cuijiao Su ◽  
Dongying Han
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Good Effect ◽  
Resonance System ◽  
Mode Decomposition ◽  
Eemd Method

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.

scholarly journals Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

2020 ◽  
Vol 10 (15) ◽  
pp. 5078
Author(s):  
Wenxiao Guo ◽  
Ruiqin Li ◽  
Yanfei Kou ◽  
Jianwei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Simulation ◽  
Random Noise ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Mining Machinery

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

Quadratic Hilbert Transform Demodulation Based on Time-Delayed Correlation Treatment and EEMD

2013 ◽  
Vol 765-767 ◽  
pp. 2715-2719 ◽  
Author(s):  
Qing Xiong ◽  
Wei Hua Zhang ◽  
Gui Ming Mei
Keyword(s):  
Hilbert Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Unbiased Estimation ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Bearing Defect ◽  
Marginal Spectrum ◽  
Repeated Analysis

To deal with the demodulation problem of rolling bearing defect vibration signal in heavy noise, a new method based on time-delayed correlation algorithm and ensemble empirical mode decomposition (EEMD) is presented. Introduced the time-delayed autocorrelation de-noising principle. After the discretization and unbiased estimation of the original signals autocorrelation function , de-noising pretreatment is implemented by appending a rectangle window. Then an envelope signal can be obtained by the first Hilbert transform. After the EEMD decomposition, some interested intrinsic mode functions (IMFs) can be collected. By making the second Hilbert transform of the IMFs, we can get the local Hilbert marginal spectrum from which the defects in a rolling bearing can be identified. By repeated analysis of simulation signals and actual rolling bearings defect vibration signal, the results show that the proposed method is more effective than direct modulation or only time-delayed correlation demodulation or combine time-delayed correlation with EMD demodulation in de-noising and diagnosing the rolling bearing's defect information.

scholarly journals A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang-qiang Chen ◽  
Shao-wu Dai ◽  
Hong-de Dai
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Main Fault

The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

scholarly journals Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 209 ◽  
Author(s):  
Shaohua Xue ◽  
Jianping Tan ◽  
Lixiang Shi ◽  
Jiwei Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Sensitivity Index ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
New Perspective

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

Rolling Bearing Fault Diagnosis Using Sample Entropy and 1.5 Dimension Spectrum Based on EMD

2013 ◽  
Vol 278-280 ◽  
pp. 1027-1031 ◽  
Author(s):  
Xian You Zhong ◽  
Chun Hua Zhao ◽  
Hai Jiang Dong ◽  
Xian Ming Liu ◽  
Liang Cai Zeng
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sample Entropy ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Dimension Spectrum ◽  
Mode Decomposition ◽  
Infor Mation

An approach of fault diagnosis of bearing based on empirical mode decomposition (EMD), sample entropy and 1.5 dimension spectrum was presented. Firstly, the original vibration signal was decomposed into a number of intrinsic mode functions (IMFs) using EMD. Second, the sample entropies of IMFs were calculated to select the sensitive IMF. Finally, the IMF containing fault infor- mation was analyzed with 1.5 dimension spectrum, The experimental results show the method can be used to effectively diagnose faults of rolling bearing.

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