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

Signal Reconstruction ◽

Low Frequency ◽

Rolling Bearing ◽

Adaptive Thresholding ◽

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.

Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Shock and Vibration ◽

10.1155/2016/3891429 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Liye Zhao ◽

Wei Yu ◽

Ruqiang Yan

Keyword(s):

Fault Diagnosis ◽

Correlation Coefficients ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Vibration Signals ◽

Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.369 ◽

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 ◽

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.

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

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b3562.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2724-2726

Keyword(s):

Fault Detection ◽

Vibration Signal ◽

Rolling Bearing ◽

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.

Random Noise Reduction Based on Ensemble Empirical Mode Decomposition and Wavelet Threshold Filtering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.3887 ◽

2012 ◽

Vol 518-523 ◽

pp. 3887-3890 ◽

Author(s):

Wei Chen ◽

Shang Xu Wang ◽

Xiao Yu Chuai ◽

Zhen Zhang

Keyword(s):

Noise Reduction ◽

High Frequency ◽

Reduction Method ◽

Random Noise ◽

Low Frequency ◽

Mode Decomposition ◽

Eemd Method

This paper presents a random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, we have conducted spectrum analysis and analyzed the frequency band range of effective signals and noise. Secondly, we make use of EEMD method on seismic signals to obtain intrinsic mode functions (IMFs) of each trace. Then, wavelet threshold noise reduction method is used on the high frequency IMFs of each trace to obtain new high frequency IMFs. Finally, reconstruct the desired signal by adding the new high frequency IMFs on the low frequency IMFs and the trend item together. When applying our method on synthetic seismic record and field data we can get good results.

Degradation Feature Extraction of Rolling Bearings Based on Optimal Ensemble Empirical Mode Decomposition and Improved Composite Spectrum Analysis

Volume 7B: Structures and Dynamics ◽

10.1115/gt2018-75041 ◽

2018 ◽

Author(s):

Fengli Wang ◽

Hua Chen

Keyword(s):

Feature Extraction ◽

Single Point ◽

Rolling Bearing ◽

Significant Feature ◽

Vibration Signals ◽

Composite Spectrum ◽

Optimal Ensemble ◽

Mode Decomposition

Rolling bearing is a key part of turbomachinery. The performance and reliability of the bearing is vital to the safe operation of turbomachinery. Therefore, degradation feature extraction of rolling bearing is important to prevent it from failure. During rolling bearing degradation, machine vibration can increase, and this may be used to predict the degradation. The vibration signals are however complicated and nonlinear, making it difficult to extract degradation features effectively. Here, a novel degradation feature extraction method based on optimal ensemble empirical mode decomposition (EEMD) and improved composite spectrum (CS) analysis is proposed. Firstly, because only a few IMFs are expected to contain the information related to bearing fault, EEMD is utilized to pre-process the vibration signals. An optimization method is designed for adaptively determining the appropriate EEMD parameters for the signal, so that the significant feature components of the faulty bearing can be extracted from the signal and separated from background noise and other irrelevant components to bearing faults. Then, Bayesian information criterion (BIC) and correlation kurtosis (CK) are employed to select the sensitive intrinsic mode function (IMF) components and obtain fault information effectively. Finally, an improved CS analysis algorithm is used to fuse the selected sensitive IMF components, and the CS entropy (CSE) is extracted as degradation feature. Experimental data on the test bearings with single point faults separately at the inner race and rolling element were studied to demonstrate the capabilities of the proposed method. The results show that it can assess the bearing degradation status and has good sensitivity and good consistency to the process of bearing degradation.

Volume 1: 22nd Biennial Conference on Mechanical Vibration and Noise, Parts A and B ◽

Rotating Machinery Fault Detection Using EEMD and Bispectrum

10.1115/detc2009-86794 ◽

2009 ◽

Author(s):

Yaguo Lei ◽

Ming J. Zuo ◽

Mohammad Hoseini

Keyword(s):

Reconstruction Algorithm ◽

Rotating Machinery ◽

Phase Coupling ◽

Third Order ◽

Vibration Signals ◽

Mode Decomposition ◽

Mixing Problem

Ensemble empirical mode decomposition (EEMD) was developed to alleviate the mode-mixing problem in empirical mode decomposition (EMD). With EEMD, the components with physical meaning can be extracted from the signal. The bispectrum, a third-order statistic, helps identify phase-coupling effects, which are useful for detecting faults in rotating machinery. Combining the advantages of EEMD and bispectrum, this paper proposes a new method for detecting such faults. First, the original vibration signals collected from rotating machinery are decomposed by EEMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMFs are reconstructed into new signals using the weighted reconstruction algorithm developed in this paper. Finally, the reconstructed signals are analyzed via the bispectrum to detect faults. Both simulation examples and gearbox experiments demonstrate that the proposed method can detect gear faults more clearly than can directly performing bispectrum analysis on the original vibration signals.

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Entropy ◽

10.3390/e22091039 ◽

2020 ◽

Vol 22 (9) ◽

pp. 1039 ◽

Author(s):

Haikun Shang ◽

Yucai Li ◽

Junyan Xu ◽

Bing Qi ◽

Jinliang Yin

Keyword(s):

Signal Reconstruction ◽

Partial Discharge ◽

Hybrid Approach ◽

Approximate Entropy ◽

Fusion Algorithm ◽

Mode Decomposition ◽

Adaptive Noise

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis ◽

10.1142/s179353691100091x ◽

2011 ◽

Vol 03 (04) ◽

pp. 483-491 ◽

Cited By ~ 7

Author(s):

BRADLEY LEE BARNHART ◽

HONDA KAHINDO WA NANDAGE ◽

WILLIAM EICHINGER

Keyword(s):

High Frequency ◽

Low Frequency ◽

Data Sets ◽

Mode Decomposition ◽

Data Gaps ◽

Discontinuous Data ◽

Periodic Components

This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets. The results show that errors increase as the gap length increases. In addition, errors in the high-frequency IMFs are less than the low-frequency IMFs. The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints. A method that utilizes a mirroring technique is presented to reduce the errors in the discontinuous decomposition. The improved algorithm provides a more locally accurate decomposition of the data amidst data gaps. Overall, this simple but powerful algorithm expands EEMD's ability to locally extract periodic components from discontinuous data.

The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.300-301.344 ◽

2013 ◽

Vol 300-301 ◽

pp. 344-350 ◽

Author(s):

Zhou Wan ◽

Xing Zhi Liao ◽

Xin Xiong ◽

Jin Chuan Han

Keyword(s):

Fault Diagnosis ◽

Low Frequency ◽

Rolling Bearing ◽

Hilbert Huang Transform ◽

Bearing Fault Diagnosis ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Eemd Method

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

Energy-based damage localization under ambient vibration and non-stationary signals by ensemble empirical mode decomposition and Mahalanobis-squared distance

Journal of Vibration and Control ◽

10.1177/1077546319891306 ◽

2019 ◽

Vol 26 (11-12) ◽

pp. 1012-1027 ◽

Cited By ~ 11

Author(s):

Hassan Sarmadi ◽

Alireza Entezami ◽

Mohammadhassan Daneshvar Khorram

Keyword(s):

Structural Damage ◽

Ambient Vibration ◽

Damage Localization ◽

Vibration Signals ◽

Mode Decomposition ◽

Stationary Signals ◽

Stationary Vibration

Damage localization of damaged structures is an important issue in structural health monitoring. In data-based methods based on statistical pattern recognition, it is necessary to extract meaningful features from measured vibration signals and utilize a reliable statistical technique for locating damage. One of the challenging issues is to extract reliable features from non-stationary vibration signals caused by ambient excitation sources. This article proposes a new energy-based method by using ensemble empirical mode decomposition and Mahalanobis-squared distance to obtain energy-based multivariate features and locate structural damage under ambient vibration and non-stationary signals. The main components of the proposed method include extracting intrinsic mode functions of vibration signals by ensemble empirical mode decomposition, choosing adequate and optimal intrinsic mode functions, partitioning the selected intrinsic mode functions at each sensor into segments with the same dimensions, calculating the intrinsic mode function energy at each segment, preparing energy-based multivariate features at each sensor, computing Mahalanobis-squared distance values, and obtaining a vector of average Mahalanobis-squared distance quantities of all sensors. The major contributions of the proposed method consist of proposing an innovative non-parametric strategy for feature extraction, presenting generalized Pearson correlation function for the selection of optimal intrinsic mode functions, using a simple and effective segmentation algorithm, and applying energy-based features to the process of damage localization. The main advantage of the proposed method is its great applicability to locating single and multiple damage cases. The measured vibration responses of the well-known IASC-ASCE structure are applied to verify the effectiveness and reliability of the proposed energy-based method along with several comparative studies. Results will demonstrate that this approach is highly capable of locating damage under stationary and non-stationary vibration signals attributable to ambient excitations.