Misalignment Characteristic Analysis Based on Advanced Empirical Mode Decomposition

2013 ◽  
Vol 325-326 ◽  
pp. 1559-1563
Author(s):  
Hui Min Li ◽  
Wei Zhao ◽  
Yun Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Rotor System ◽  
Experimental Results ◽  
Intrinsic Mode Functions ◽  
Characteristic Analysis ◽  
Wavelet Method ◽  
Vibration Signals ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Misalignment Fault

A new method of misalignment characteristic analysis, which is based on advanced empirical mode decomposition (AEMD), is presented in this paper. At first the vibration signals of a rotor system with different misalignments is collected separately. Then the multicomponent signal x (t) is decomposed into a number of the so-called intrinsic mode functions (IMFs) by use of AEMD respectively. For these IMFs the wavelet method is used to extract the interesting features. It is found that the IMF2 contains the interesting misalignment character. Additionally the experimental results substantiate that the proposed method for misalignment analysis can identify the varying trend of misalignment fault clearly.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

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.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

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.

Analysis of in-flight cabin vibration of a turboprop airplane by proposing a novel noise-tolerant signal decomposition method

2021 ◽  
pp. 107754632110075
Author(s):  
Seyed Amin Bagherzadeh ◽  
Mehdi Salehi
Keyword(s):  
Empirical Mode Decomposition ◽  
Computational Cost ◽  
Quality Criteria ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Local Maxima ◽  
Numerical Studies ◽  
Mode Decomposition ◽  
Lower Envelopes ◽  
Noise Tolerant

Vibration in passenger cabins of turboprop airplanes is a serious challenge. One of the essential steps in studying the cabin vibrations is to determine the contributing sources of vibration. The vibration signals are highly nonstationary and noisy. Therefore, one may require a noise-tolerant signal processing method for decomposition of the signals. In this article, the wavelet-based empirical mode decomposition is introduced for the first time to improve the performance of the traditional empirical mode decomposition in dealing with noise. Unlike the traditional empirical mode decomposition that extracts the signal trend by averaging the upper and lower envelopes intersecting local maxima and minima of the signal, the wavelet-based empirical mode decomposition directly extracts the signal trend by applying the multilevel wavelet decomposition of the consecutive approximations within the sifting process. Numerical studies are undertaken to evaluate the effect of noise on the performance of the empirical mode decomposition and wavelet-based empirical mode decomposition. Also, comparisons are made between the methods at dissimilar noise powers based on the orthogonality, integral, and energy decomposition criteria. The results indicate that both methods generate similar results in the absence of noise. Considering the number of obtained intrinsic mode functions, decomposition quality criteria, and computational cost, however, the wavelet-based empirical mode decomposition outperforms the classic method at higher noise levels. In this article, the wavelet-based empirical mode decomposition is used for analysis of in-flight airplane cabin vibration. A 52-passenger turboprop aircraft is equipped with eight triaxial piezoelectric accelerometers, and several flight tests are performed to acquire in-flight vibration signals within the passenger cabin. The proposed wavelet-based empirical mode decomposition is applied to the experimental data. Then, the amplitudes and frequencies of the intrinsic mode functions are examined. Finally, the probable vibration sources are identified based on the intrinsic mode functions characteristics.

Development of Diagnosis System for Machine Tool Shaft Inspection

2010 ◽  
Vol 156-157 ◽  
pp. 1717-1724
Author(s):  
Nan Kai Hsieh ◽  
Wei Yen Lin ◽  
Hong Tsu Young
Keyword(s):  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Zero Crossing ◽  
Vibration Signals ◽  
Diagnosis System ◽  
Mode Decomposition ◽  
Faults Diagnosis ◽  
Zero Crossing Rate ◽  
Emd Method

Aiming at reducing cost and time of repair, condition-based shaft faults diagnosis is considered an efficient strategy for machine tool community. While the shaft with faults is operating, its vibration signals normally indicate nonlinear and non-stationary characteristics but Fourier-based approaches have shown limitations for handling this kind of signals. The methodology proposed in this research is to extract the features from shaft faults related vibration signals, from which the corresponding fault condition is then effectively identified. With an incorporation of empirical mode decomposition (EMD) method, the model applied in this research embraces some characteristics, like zero-crossing rate and energy, of intrinsic mode functions (IMFs) to represent the feature of the shaft condition.

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.

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