Rolling Bearing Fault Degree Recognition Based on Ensemble Empirical Mode Decomposition and Support Vector Regression

2013 ◽  
Vol 333-335 ◽  
pp. 550-554 ◽  
Author(s):  
Chang Qing Shen ◽  
Fei Hu ◽  
Zhong Kui Zhu ◽  
Fan Rang Kong
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Bearing Fault ◽  
Mode Decomposition

The research in bearing fault diagnosis has been attracting great attention in the past decades. Development of feasible fault diagnosis procedures to prevent failures that could cause huge economic loss timely is necessary. The whole life of the bearing is also a developing process for some sensitive features related to the fault trend. In this paper, a new scheme based on ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to conduct bearing fault degree recognition is proposed. This analysis first extracts the sensitive features from the intrinsic mode functions (IMFs) produced by EEMD which is a potential time-frequency analysis method, and then constructs an intelligent nonlinear model with input feature vectors extracted from the IMFs and defect size as output. Through validation of experimental data, the results indicated that the bearing fault degree could be effectively and precisely recognized.

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 Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

2020 ◽  
Vol 10 (16) ◽  
pp. 5542 ◽  
Author(s):  
Rui Li ◽  
Chao Ran ◽  
Bin Zhang ◽  
Leng Han ◽  
Song Feng
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Operational Conditions ◽  
Mode Decomposition ◽  
Adaptive Noise

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Spatiotemporal Trend and Variability of Precipitation in Taiwan Based on Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD)

2021 ◽  
Author(s):  
Chun-Hsiang Tang ◽  
Christina W. Tsai
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Time Frequency ◽  
Spatial Locality ◽  
Temperature And Precipitation ◽  
Mode Decomposition ◽  
Climatic Scenarios

<p>Abstract</p><p>Most of the time series in nature are nonlinear and nonstationary affected by climate change particularly. It is inevitable that Taiwan has also experienced frequent drought events in recent years. However, drought events are natural disasters with no clear warnings and their influences are cumulative. The difficulty of detecting and analyzing the drought phenomenon remains. To deal with the above-mentioned problem, Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) is introduced to analyze the temperature and rainfall data from 1975~2018 in this study, which is a powerful method developed for the time-frequency analysis of nonlinear, nonstationary time series. This method can not only analyze the spatial locality and temporal locality of signals but also decompose the multiple-dimensional time series into several Intrinsic Mode Functions (IMFs). By the set of IMFs, the meaningful instantaneous frequency and the trend of the signals can be observed. Considering stochastic and deterministic influences, to enhance the accuracy this study also reconstruct IMFs into two components, stochastic and deterministic, by the coefficient of auto-correlation.</p><p>In this study, the influences of temperature and precipitation on the drought events will be discussed. Furthermore, to decrease the significant impact of drought events, this study also attempts to forecast the occurrences of drought events in the short-term via the Artificial Neural Network technique. And, based on the CMIP5 model, this study also investigates the trend and variability of drought events and warming in different climatic scenarios.</p><p> </p><p>Keywords: Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD), Intrinsic Mode Function(IMF), Drought</p>

A fault diagnosis method of rolling element bearings based on CEEMDAN

2015 ◽  
Vol 231 (10) ◽  
pp. 1804-1815 ◽  
Author(s):  
Yaguo Lei ◽  
Zongyao Liu ◽  
Julien Ouazri ◽  
Jing Lin
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Noisy Signals ◽  
Diagnosis Method

Ensemble empirical mode decomposition (EEMD) represents a valuable aid in empirical mode decomposition (EMD) and has been widely used in fault diagnosis of rolling element bearings. However, the intrinsic mode functions (IMFs) generated by EEMD often contain residual noise. In addition, adding different white Gaussian noise to the signal to be analyzed probably produces a different number of IMFs, and different number of IMFs makes difficult the averaging. To alleviate these two drawbacks, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was previously presented. Utilizing the advantages of CEEMDAN in extracting weak characteristics from noisy signals, a new fault diagnosis method of rolling element bearings based on CEEMDAN is proposed. With this method, a particular noise is added at each stage and after each IMF extraction, a unique residue is computed. In this way, this method solves the problem of the final averaging and obtains IMFs with less noise. A simulated signal is used to illustrate the effectiveness of the proposed method, and the decomposition results show that the method obtains more accurate IMFs than the EEMD. To further demonstrate the proposed method, it is applied to fault diagnosis of locomotive rolling element bearings. The diagnosis results prove that the method based on CEEMDAN may reveal the fault characteristic information of rolling element bearings better.

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