Diagnosis of Low-Speed Helical Gears Based on Hilbert Demodulation and Empirical Mode Decomposition

2013 ◽  
Vol 572 ◽  
pp. 443-446
Author(s):  
Bao Yu Song ◽  
Zhi Jie Xie ◽  
Feng Zhang ◽  
Jin Ping Yang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Helical Gears ◽  
Low Speed ◽  
Mode Decomposition ◽  
Vibration Signal Analysis

As the vibration signal of helical gears is nonlinear and nonstationary, it is very difficult to diagnose their faults based on the vibration signal analysis and processing, particularly when the gears rotate at low speed. In this paper, an applicable fault diagnosis approach is proposed based on Hilbert demodulation and EMD (empirical mode decomposition). Firstly, the modulated signals are extracted through Hilbert envelope demodulationand low pass filter. Furthermore, EMD is used to decompose the multi-component demodulated signal into a series of intrinsic mode functions (IMFs) whose instantaneous frequencies have a physically meaningful characterization of the original signal. Finally, the fault features of low-speed helical gear are obtained by the spectrum analysis to each IMF. The experiments of tooth broken fault diagnosis show that this method is more effective than traditional Hilbert demodulation analysis.

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.

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.

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.

Vibration Signal Analysis and Fault Diagnosis of Gears Based on Hilbert Marginal Spectrum

2014 ◽  
Vol 909 ◽  
pp. 121-126 ◽  
Author(s):  
Jiang Ping Wang ◽  
Jin Cui
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Characteristic Time Scale ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

Hilbert-Huang transform is a new method of signal processing, which is very suitable for dealing with nonlinear and non-stationary signal. In this article, a gear fault diagnosis method based on Hilbert marginal spectrum is proposed in view of the non-stationary characteristics of gear vibration signal. First the original vibration signal is decomposed into several intrinsic mode functions (IMF) of different characteristic time scale smoothly by means of empirical mode decomposition (EMD) method. Then the Hilbert-Huang transform is carried out for IMF and the Hilbert marginal spectrum under different operating conditions are obtained. Gear faults can be judged through the analysis of the marginal spectrum. The experimental results show that this method can effectively diagnose the gear faults.

Gear Fault Diagnosis Method Based on the Integration of Empirical Mode Decomposition and Cepstrum Analysis

2013 ◽  
Vol 310 ◽  
pp. 328-333 ◽  
Author(s):  
Bing Luo ◽  
Wen Tong Yang ◽  
Zhi Feng Liu ◽  
Yong Sheng Zhao ◽  
Li Gang Cai
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Mechanical Transmission ◽  
Intrinsic Mode Functions ◽  
Cepstrum Analysis ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

Gear is the most common mechanical transmission equipment. Therefore, gear fault diagnosis is of much significance. In this article, a gear fault diagnosis method based on the integration of empirical mode decomposition and cepstrum is proposed by introducing empirical mode decomposition and cepstrum into gear fault analysis. Firstly EMD is used to decompose the gear vibration signal finite number of intrinsic mode functions and a residual error item. To do gear fault diagnosis, cepstrum analysis is carried upon those intrinsic mode functions to extract feature information from the vibration signal. The results of the study on simulated and experimental signals show that this method is better than the cepstrum method and it can precisely locate the site of gear failure.

Rolling Bearing Fault Diagnosis Using Sample Entropy and 1.5 Dimension Spectrum Based on EMD

2013 ◽  
Vol 278-280 ◽  
pp. 1027-1031 ◽  
Author(s):  
Xian You Zhong ◽  
Chun Hua Zhao ◽  
Hai Jiang Dong ◽  
Xian Ming Liu ◽  
Liang Cai Zeng
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sample Entropy ◽  
Intrinsic Mode Functions ◽  
Bearing Fault Diagnosis ◽  
Dimension Spectrum ◽  
Mode Decomposition ◽  
Infor Mation

An approach of fault diagnosis of bearing based on empirical mode decomposition (EMD), sample entropy and 1.5 dimension spectrum was presented. Firstly, the original vibration signal was decomposed into a number of intrinsic mode functions (IMFs) using EMD. Second, the sample entropies of IMFs were calculated to select the sensitive IMF. Finally, the IMF containing fault infor- mation was analyzed with 1.5 dimension spectrum, The experimental results show the method can be used to effectively diagnose faults of rolling bearing.

A fault diagnosis method for rolling element bearing (REB) based on reducing REB foundation vibration and noise-assisted vibration signal analysis

2018 ◽  
Vol 233 (7) ◽  
pp. 2574-2587 ◽  
Author(s):  
Heng-di Wang ◽  
Si-er Deng ◽  
Jian-xi Yang ◽  
Hui Liao
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Foundation Vibration ◽  
Diagnosis Method ◽  
Element Bearing ◽  
Vibration Signal Analysis

Owing to the problem of the incipient fault characteristics being difficult to be extracted from the raw vibration signal of rolling element bearing, based on the empirical mode decomposition and kurtosis criteria, a fault diagnosis method for rolling element bearing is proposed by reducing rolling element bearing foundation vibration and noise-assisted vibration signal analysis. Firstly, rolling element bearing vibration signal is decomposed into a set of intrinsic mode functions using empirical mode decomposition and the intrinsic mode function component with the maximal kurtosis value is selected. Afterwards, zero mean normalization is applied to the selected intrinsic mode function component, and then the intrinsic mode function’s foundation vibration components within [Formula: see text] are removed to minimize the interference. In order to eliminate interruption and intermittency after removal of the foundation vibration components, white noise is added to the newly generated signal. The noise-added signal is decomposed via empirical mode decomposition, and later on, IMF1 with the highest frequency band is selected and demodulated using envelope analysis. The resulting envelope spectrum can show more significant fault pulse characteristics, which are highly helpful to diagnose the rolling element bearing incipient faults. The proposed method in this paper was applied to the fault diagnosis for low noise REB 6203 and the testing results showed that the method could identify the rolling element bearing incipient faults accurately and quickly.

Two-dimensional empirical mode decomposition by finite elements

2006 ◽  
Vol 462 (2074) ◽  
pp. 3081-3096 ◽  
Author(s):  
Y Xu ◽  
B Liu ◽  
J Liu ◽  
S Riemenschneider
Keyword(s):  
Finite Element ◽  
Linear Combination ◽  
Empirical Mode Decomposition ◽  
Spline Interpolation ◽  
Basis Functions ◽  
Two Dimensional ◽  
Element Analysis ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Mode Decomposition

Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationary and nonlinear signals, and has drawn significant attention in various engineering application areas. This paper presents a finite element-based EMD method for two-dimensional data analysis. Specifically, we represent the local mean surface of the data, a key step in EMD, as a linear combination of a set of two-dimensional linear basis functions smoothed with bi-cubic spline interpolation. The coefficients of the basis functions in the linear combination are obtained from the local extrema of the data using a generalized low-pass filter. By taking advantage of the principle of finite-element analysis, we develop a fast algorithm for implementation of the EMD. The proposed method provides an effective approach to overcome several challenging difficulties in extending the original one-dimensional EMD to the two-dimensional EMD. Numerical experiments using both simulated and practical texture images show that the proposed method works well.

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 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.

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