scholarly journals Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Time Series ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Test System ◽  
Ar Model ◽  
Model Parameters ◽  
Time Series Modeling ◽  
Intrinsic Mode Functions ◽  
Vibration Signals

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

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.

scholarly journals Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 519 ◽  
Author(s):  
Weibo Zhang ◽  
Jianzhong Zhou
Keyword(s):  
Diagnostic Algorithm ◽  
Feature Space ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

2013 ◽  
Vol 347-350 ◽  
pp. 430-433
Author(s):  
Wen Bin Zhang ◽  
Jia Xing Zhu ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Rank Order ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Small Sample ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Gearbox Vibration

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

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.

The Fault Pattern Identification of Hydraulic Pump Based on the Sensitive Component STSA

2014 ◽  
Vol 635-637 ◽  
pp. 790-794
Author(s):  
Yu Kui Wang ◽  
Hong Ru Li ◽  
Peng Ye
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hydraulic Pump ◽  
Mode Decomposition ◽  
Novel Method ◽  
Symbolic Time Series Analysis

A novel method which is based on ensemble empirical mode decomposition (EEMD) and symbolic time series analysis (STSA) was proposed in this paper. Firstly, the vibration signal of hydraulic pump was decomposed into a number of stationary intrinsic mode functions (IMFs). Secondly, the sensitive component was extracted. Finally, the relative entropy (RE) was extracted from the sensitive components and they were used as the indicator to distinguish the faults of hydraulic pump. The research results of actual testing vibration signal demonstrated the rationality and effectiveness of the proposed method in this paper.

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.

Fault Diagnosis of Elevator Based AR Bi-Spectrum

2012 ◽  
Vol 468-471 ◽  
pp. 1743-1748
Author(s):  
Jing Yu Yi ◽  
Yi Jian Huang
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Gaussian White Noise ◽  
Vibration Signal ◽  
Ar Model ◽  
Vibration Signals ◽  
Working Status ◽  
Non Gaussian ◽  
Analysis Of Time Series

According to the characteristics of the elevator fault vibration signals, proposing a Based on analysis of time series AR bi-spectrum elevator fault diagnosis. When the zero-mean, non-Gaussian white noise elevator device, the vibration signal using sampling to establish time series autoregressive model (AR model), resulting in AR bi-spectrum. Bi-spectrum signal processing is a new, powerful signal processing technology, which can be described the nonlinear coupling, suppression Gaussian noise and retention of phase information, you can get the elevator working status of the different dynamic characteristics. The results show that bi-spectral analysis with AR elevator failure is feasible and effective.

scholarly journals EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
David Camarena-Martinez ◽  
Juan P. Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez ◽  
Rene J. Romero-Troncoso ◽  
Roque A. Osornio-Rios ◽  
...  
Keyword(s):  
Natural Frequencies ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Operating Conditions ◽  
Ambient Vibration ◽  
Intrinsic Mode Functions ◽  
Highway Traffic ◽  
Vibration Signals ◽  
Multiple Signal Classification ◽  
Mode Decomposition

This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.

Sign in / Sign up

Export Citation Format

Share Document