Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis

2018 ◽  
Vol 424 ◽  
pp. 192-207 ◽  
Author(s):  
Dongyue Chen ◽  
Jianhui Lin ◽  
Yanping Li
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Evaluation Index ◽  
High Speed Train ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition

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.

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.

scholarly journals Blade fault diagnosis using empirical mode decomposition based feature extraction method

2019 ◽  
Vol 255 ◽  
pp. 06009
Author(s):  
C. Y. Tan ◽  
W. K. Ngui ◽  
M. S. Leong ◽  
M. H. Lim
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Feature Vector ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Statistical Parameters ◽  
Mode Decomposition ◽  
Parameters Extraction

Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are usually used as the input to the artificial neural network for blade fault diagnosis. However, the application of others time-frequency based feature extraction technique and artificial intelligence approach for blade fault diagnosis is still lacking. In this study, a novel blade fault diagnosis method based on ensemble empirical mode decomposition and extreme learning machine was developed. Bandpass filtering was applied to the raw vibration signals and integrated with the high pass filter to obtain the velocity signal. Synchronous time averaging was then applied to the velocity signals. Three ensemble empirical mode decomposition based feature extraction methods were proposed: direct statistical parameters extraction, intrinsic mode functions averaging statistical parameters extraction and features averaging statistical parameters extraction. The effectiveness of different feature vector sets for blade fault diagnosis was examined. Feature vector set of intrinsic mode functions averaging statistical parameters extraction was found to be more effective for blade fault diagnosis. With the novel proposed method, blade fault diagnosis could be more accurate and precise.

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 VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 228 ◽  
Author(s):  
Xiaobo Bi ◽  
Jiansheng Lin ◽  
Daijie Tang ◽  
Fengrong Bi ◽  
Xin Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Empirical Mode Decomposition ◽  
Fault Simulation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
K Value ◽  
Mode Decomposition

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

scholarly journals Real Time Cardan Shaft State Estimation of High-Speed Train Based on Ensemble Empirical Mode Decomposition

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Cai Yi ◽  
Jianhui Lin ◽  
Tengda Ruan ◽  
Yanping Li
Keyword(s):  
Real Time ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Estimation Method ◽  
Ensemble Empirical Mode Decomposition ◽  
High Speed Train ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Special Location

Due to the special location and structure of transmission system on high-speed train named CRH5, dynamic unbalance state of the cardan shaft will pose a threat to the train servicing safety, so effective methods that test the cardan shaft operating information and estimate the performance state in real time are needed. In this study a useful estimation method based on ensemble empirical mode decomposition (EEMD) is presented. By using this method, time-frequency characteristic of cardan shaft can be extracted effectively by separating the gearbox vibration acceleration data. Preliminary analysis suggests that the pinions rotating vibration separated from gearbox vibration by EEMD can be used as important assessment basis to estimate cardan shaft state. With two sets gearbox vibration signals collected from the in-service train at different running speed, the comparative analysis verifies that the proposed method has high effectiveness for cardan-shaft state estimate. Of course, it needs further research to quantify the performance state of cardan shaft based on this method.

scholarly journals Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN

2017 ◽  
Vol 4 (8) ◽  
pp. 170616 ◽  
Author(s):  
Vanraj ◽  
S. S. Dhami ◽  
B. S. Pabla
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Operating Conditions ◽  
Intrinsic Mode Functions ◽  
Statistical Parameters ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Wide Range ◽  
Fixed Axis

Gearbox plays most essential role in the modern machinery for transmitting the required torque along with motion and contributes to wide range of applications. Any failure in gearbox components affects the productivity and efficiency of the system. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence lead to failure of the whole mechanism. Ensemble Empirical Mode Decomposition (EEMD) comprises advancement and valuable addition in Empirical Mode Decomposition (EMD) and has been widely used in fault detection of rotating machines. However, intrinsic mode functions (IMFs) produced by EEMD often carry the residual noise. Also, the produced IMFs are different in number due to addition of white Gaussian noise, which leads to final averaging problem. To alleviate these drawbacks, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was previously presented. This paper describes and presents the implementation of CEEMDAN for fault diagnosis of simulated local defects using sound signals in a fixed-axis gearbox. Statistical parameters are extracted from decomposed sound signals for different simulated faults. Results show the effectiveness of CEEMDAN over EEMD in order to obtain more accurate IMFs and fault severity.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

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