Rotating Machinery Fault Detection Using EEMD and Bispectrum

2009 ◽  
Author(s):  
Yaguo Lei ◽  
Ming J. Zuo ◽  
Mohammad Hoseini
Keyword(s):  
Empirical Mode Decomposition ◽  
Reconstruction Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
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.

The use of ensemble empirical mode decomposition to improve bispectral analysis for fault detection in rotating machinery

2010 ◽  
Vol 224 (8) ◽  
pp. 1759-1769 ◽  
Author(s):  
Y Lei ◽  
M J Zuo ◽  
M Hoseini
Keyword(s):  
Empirical Mode Decomposition ◽  
Reconstruction Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Physical Experiments ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Mode Mixing

Empirical mode decomposition (EMD) has been widely applied to analyse signals for the detection of faults in rotating machinery. However, sometimes, it cannot reveal signal characteristics accurately because of the mode mixing problem. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate the mode mixing problem of EMD. With EEMD, components that are physically meaningful can be extracted from the signals. Bispectrum, a third-order statistic, helps identify phase coupling effects, which are useful for detecting faults in rotating machinery. Utilizing the advantages of EEMD and bispectrum, this article proposes a joint method for detecting such faults. First, 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 article. Finally, the reconstructed signals are analysed via bispectrum to detect faults. The simulation experiments and the physical experiments of two gears with a chipped tooth and a cracked tooth, respectively, demonstrate that the proposed method can detect faults more clearly than can directly performing bispectrum on the original vibration signals.

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

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
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.

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 Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Xie ◽  
Tao Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Spatial Information ◽  
Health Management ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

Purification of Axis Trace by Ensemble Empirical Mode Decomposition

2013 ◽  
Vol 791-793 ◽  
pp. 1006-1009
Author(s):  
Jia Xing Zhu ◽  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
New Method ◽  
Intrinsic Mode Functions ◽  
Mode Function ◽  
Mode Decomposition ◽  
Novel Method

Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and reconstructed the signal. Finally the purification of axis trace was obtained. Simulation and practical results show the advantage of ensemble empirical mode decomposition. This method also has simple algorithm and high calculating speed; it provides a new method for purification of axis trace.

scholarly journals A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 9 (3) ◽  
pp. 461-466
Keyword(s):  
Empirical Mode Decomposition ◽  
Distributed Generation ◽  
Gaussian White Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Islanding Detection ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
Mixing Problem ◽  
Mode Mixing

Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events

A New Purification Method for Rotor Center’s Orbit by Using Ensemble Empirical Mode Decomposition

2014 ◽  
Vol 986-987 ◽  
pp. 801-804
Author(s):  
Wen Bin Zhang ◽  
Jia Xing Zhu ◽  
Ya Song Pu ◽  
Yan Ping Su
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Purification Method ◽  
New Approach ◽  
Mode Decomposition ◽  
Useful Signal

. Aiming at the purification of rotor center’s orbit, a new approach was presented by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a series of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose some interested IMFs and reconstructed the needed signal. By doing this the noises would be eliminated successfully. At last the purification of rotor center’s orbit was obtained by extracting the useful signal component. Simulation and practical results show the advantage of EEMD in noise de-noising and purification of rotor center’s orbit. This method also has simple algorithm and high calculating speed; it provides a new way for purification of rotor center’s orbit of rotating machinery.

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

2019 ◽  
Vol 26 (11-12) ◽  
pp. 1012-1027 ◽  
Author(s):  
Hassan Sarmadi ◽  
Alireza Entezami ◽  
Mohammadhassan Daneshvar Khorram
Keyword(s):  
Empirical Mode Decomposition ◽  
Structural Damage ◽  
Ensemble Empirical Mode Decomposition ◽  
Ambient Vibration ◽  
Damage Localization ◽  
Intrinsic Mode Functions ◽  
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.

Fault Diagnosis of Rotating Machinery Based on Empirical Mode Decomposition and Fractal Feature Parameter Classification

2010 ◽  
Vol 439-440 ◽  
pp. 658-663 ◽  
Author(s):  
Jiang Tao Huang ◽  
Xiao Wen Cao ◽  
Wu Jin Li
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Discriminant Function ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Quadratic Discriminant Function ◽  
Feature Parameter

Rolling bearings are vital elements in rotating machinery and vibration signal is a kind of effective mean to characterize the status of rolling bearing fault. This paper presents a novel intelligent method for fault diagnosis based on empirical mode decomposition, fractal feature parameter extracting and orthogonal quadratic discriminant function classifier. The new method consists of three steps. Firstly, with investigating the feature of impact fault in vibration signals, the raw vibration signals are decomposed into intrinsic mode functions by empirical mode decomposition. Secondly, using the method of time sequences fractal dimension calculating, fractal feature parameters are extracted from intrinsic mode functions. Then, each raw signal sample has a feature set. Finally, training set and testing set are inputted into the orthogonal quadratic discriminant function model in the classification phase to identify different abnormal cases. The proposed method is applied to the fault diagnosis of rolling element bearing, and the test results indicate that the novel intelligent diagnosis method is sensitive to fault severity and capable of fault detection and fault diagnosis.

scholarly journals Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254747
Author(s):  
Kangping Gao ◽  
Xinxin Xu ◽  
Jiabo Li ◽  
Shengjie Jiao ◽  
Ning Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Comprehensive Evaluation ◽  
Polycrystalline Diamond ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Polycrystalline Diamond Compact

Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

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