scholarly journals Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jia ◽  
Fuqi Ma ◽  
Jian Dang ◽  
Guangyi Liu ◽  
Huizhi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Empirical Mode Decomposition ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Large Wind

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

scholarly journals A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 680 ◽  
Author(s):  
Zhang ◽  
Zhou
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Rolling Bearings ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Candidate Feature

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

scholarly journals Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiakai Ding ◽  
Dongming Xiao ◽  
Liangpei Huang ◽  
Xuejun Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hopfield Neural Network ◽  
Sample Entropy ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

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.

scholarly journals A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 995 ◽  
Author(s):  
Tao Liang ◽  
Hao Lu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Rolling Bearings ◽  
Feature Extraction Method ◽  
Mode Decomposition

Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

scholarly journals Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Te Han ◽  
Dongxiang Jiang
Keyword(s):  
Random Forest ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ar Model ◽  
Model Parameters ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Non Gaussian

Targeting the nonstationary and non-Gaussian characteristics of vibration signal from fault rolling bearing, this paper proposes a fault feature extraction method based on variational mode decomposition (VMD) and autoregressive (AR) model parameters. Firstly, VMD is applied to decompose vibration signals and a series of stationary component signals can be obtained. Secondly, AR model is established for each component mode. Thirdly, the parameters and remnant of AR model served as fault characteristic vectors. Finally, a novel random forest (RF) classifier is put forward for pattern recognition in the field of rolling bearing fault diagnosis. The validity and superiority of proposed method are verified by an experimental dataset. Analysis results show that this method based on VMD-AR model can extract fault features accurately and RF classifier has been proved to outperform comparative classifiers.

Research on Fault Diagnosis of Drive Train in Wind Turbine Based on EMD and LSSVM

2012 ◽  
Vol 512-515 ◽  
pp. 763-770 ◽  
Author(s):  
Hui Liu ◽  
Chao Wang ◽  
Wen Jun Yan
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wind Turbines ◽  
Extraction Method ◽  
Drive Train ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Diagnosis Accuracy ◽  
Diagnosis Model

Fault feature extraction method based on Empirical Mode Decomposition (EMD) and fault diagnosis model based on Least Squares Support Vector Machines (LSSVM) were proposed after typical faults in drive train for wind turbines being analyzed. An experiment was designed to verify the validity of feature extraction method and the intelligent diagnosis model. The results showed that EMD can effectively extract fault characteristics of the drive train in wind turbines, the classification speed and diagnosis accuracy of LSSVM classifier based on radial basis function are better than the SVM, BPNN and other classifiers which are commonly used in practice.

An Ensemble Empirical Mode Decomposition-Based Lossy Signal Compression Method for a Remote and Wireless Bearing Condition Monitoring System

2012 ◽  
Author(s):  
Peter W. Tse ◽  
Wei Guo
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Signal Compression ◽  
Compression Method ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Bearing Vibration ◽  
Eemd Method

Rolling bearings are one of the most widely used and most likely to fail components in the vast majority of rotating machines. A remote and wireless bearing condition system allows the bearings to be inspected in remote or hazardous environments and increases the machine reliability. To minimize the transmission loads of enormous vibration data for accurate bearing fault diagnosis, a lossy compression method based on ensemble empirical mode decomposition (EEMD) method was proposed for bearing vibration signals in this paper. The EEMD method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different frequency bands of signal components called intrinsic mode functions (IMFs). After applying the EEMD method to the vibration signal, the impulsive signal component related to the faulty bearing is extracted. The noise and irrelevant signal components that are often embedded in the collected vibration signals were removed. In the bearing signal, the distribution for most of the extremes is around zero. Almost all meaningful extremes related to the defect are concentrated in a small fraction of the samples. Hence, this signal compression provides high compression ratio for the bearing vibration signal. To verify the effectiveness of this method, raw vibration signals were collected from an experimental motor and a real traction motor. The proposed lossy signal compression method was applied to these vibration signals to extract the bearing signals and compress them. A comparison of this compression method with the popular wavelet compression method was also conducted. Wirelessly transmitting these compressed data demonstrates that the proposed signal compression method provides high compression performance for bearing vibration signals. Furthermore, the fault diagnosis using the reconstructed signal indicates that most of the impulses relating to the bearing fault are retained, including their periodicity and amplitudes, which are vital for accurate bearing fault diagnosis. Therefore, the compression of the bearing vibration signal contributes not only on the decreases of the file size and the transmission time, but also on the extraction of faulty bearing features to improve the accuracy in signal analysis. With the help of this method, wireless data communication for the remote and wireless bearing condition monitoring system becomes highly efficient, even in a limited bandwidth environment and maintains accurate bearing fault detection without loss of features and the need of transmitting a large amount of vibration data.

Gear Local Fault Diagnosis with Empirical Mode Decomposition and Hilbert Huang Transformation

2011 ◽  
Vol 199-200 ◽  
pp. 899-904 ◽  
Author(s):  
Zhen Nan Han ◽  
Jian Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Instantaneous Frequency ◽  
Signal Analysis ◽  
Physical Simulation ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Vibration Energy ◽  
Mode Decomposition ◽  
Vibration Signal Analysis

A new method for gear local fault diagnosis based on vibration signal analysis is presented in this paper by using the concept of instantaneous frequency. The data from the physical simulation are used to detect the change in the instantaneous frequency and meshing vibration energy of the gear tooth fault by Empirical Mode Decomposition and Hilbert Huang Transformation (EMD-HHT). It is verified that method is effective by rig testing of geared system.

scholarly journals Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianhua Cai ◽  
Xiaoqin Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Correlation Coefficient ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Dimension Spectrum ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault

Aiming at the nonlinear and nonstationary feature of mechanical fault vibration signal, a new fault diagnosis method, which is based on a combination of empirical mode decomposition (EMD) and 1.5 dimension spectrum, is proposed. Firstly, the vibration signal is decomposed by EMD and the correlation coefficient between each intrinsic mode function and original signal is calculated. Then these intrinsic mode function components, which have a big correlation coefficient, are selected to estimate its 1.5 dimension spectrum. And this method uses 1.5 dimension spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums are summed to obtain the primary power spectrum of gear fault signal. Finally, the information feature of fault is extracted from the reconstructed 1.5 dimension spectrum. A model to reconstruct 1.5 dimension spectrum is established, and the principle and steps of the method are presented. Some simulated and measured gear fault signals have been processed to demonstrate the effectiveness of new method. The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.

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