Gearbox Fault Diagnosis Based on Empirical Mode Decomposition and Hilbert Transform

2012 ◽  
Vol 542-543 ◽  
pp. 238-241
Author(s):  
Yan Li Liu ◽  
De Xiang Zhang ◽  
Ming Wei Ji
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Intrinsic Mode Function ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Wide Range ◽  
Vibration Signal Analysis

Gearbox is vital components in a wide range of industrial and transport applications. It is very important how to monitor operating state of automobile gearbox and detect incipient faults. This paper applies the empirical mode decomposition (EMD) and Hilbert spectrum methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then Hilbert spectrum of intrinsic mode function at different fault characteristic frequencies is obtained by Hilbert transform. Finally, the time-frequency fault characteristics of gearbox are analyzed by the Hilbert spectrum value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and Hilbert spectrum characteristic method in fault diagnosis and fault message abstraction.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

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.

Approach to extracting gear fault feature based on dominant intrinsic mode function

2014 ◽  
Vol 228 (15) ◽  
pp. 2810-2819 ◽  
Author(s):  
Zhifeng Liu ◽  
Bing Luo ◽  
Wentong Yang ◽  
Ligang Cai ◽  
Jingying Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Good Time ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Rate Evaluation ◽  
Instantaneous Energy

Complex nonlinear and nonstationary signals can be adaptively analyzed by the Hilbert–Huang transform through empirical mode decomposition and the Hilbert transform to generate the instantaneous energy. The instantaneous energy was able to display the local characteristics of the signals and had good time–frequency analysis capability, it is therefore widely applied to the analysis of vibration signals in the field of gear fault diagnosis. However, only a few extracted intrinsic mode functions through empirical mode decomposition can reflect fault feature or closely related to the faults but others are irrelevant. Therefore, the fault feature of the instantaneous energy for all intrinsic mode functions was not obvious and the accuracy of diagnosis was low. Aimed at solving this problem, a fault leading rate evaluation algorithm was proposed that can select those intrinsic mode functions, which reflect fault features (it was called the dominant intrinsic mode function) from all intrinsic mode functions. In the paper, this algorithm was applied to gear fault feature extraction. By calculating the instantaneous energy of the dominant intrinsic mode function the method could accurately extract gear fault feature and improve the accuracy of diagnosis. Both simulated signals and experimental signals of a Klingelnberg bevel gear were analyzed to verify the effectiveness and correctness of the algorithm.

Research on Fault Diagnosis Method Based on Empirical Mode Decomposition & Time-Frequency Reassignment

2012 ◽  
Vol 433-440 ◽  
pp. 6256-6261
Author(s):  
Zhi Hua Hao ◽  
Zhuang Ma ◽  
Hao Miao Zhou
Keyword(s):  
Empirical Mode Decomposition ◽  
Simulation Analysis ◽  
Intrinsic Mode Function ◽  
Time Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Frequency Representation ◽  
Cross Terms ◽  
Decomposition Time ◽  
The Cross

The reassignment method is a technique for sharpening a time-frequency representation by mapping the data to time-frequency coordinates that are nearer to the true region of support of the analyzed signal. The reassignment method has been proved to produce a better localization of the signal components and improve the readability of the time-frequency representation by concentrating its energy at a center of gravity. But there are still few cross-terms. Then, the empirical mode decomposition is introduced to the reassignment method to suppress the interference of the cross-term encountered in processing the multi-component signals. The multi-component signal can be decomposed into a finite number intrinsic mode function by using EMD. Then, the reassignment method can be calculated for each of the intrinsic mode function. Simulation analysis is presented to show that this method can improve the localization of time-frequency representation and reduce the cross terms. The vibration signals measured from diesel engine in the stage of deflagrate were analyzed with the reassignment method. Experimental results indicate that this method has good potential in mechanical fault feature extraction.

Over-Sifting Components Analysis in Bidimensional Empirical Mode Decomposition

2010 ◽  
Vol 159 ◽  
pp. 377-382
Author(s):  
Guang Tao Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Mean ◽  
Components Analysis ◽  
Bidimensional Empirical Mode Decomposition

Define the course of getting mean envelope as an operation (mean envelope operation) in Empirical mode decomposition (EMD), so as to express the Intrinsic Mode Function (IMF) with mean envelopes. Summarize several rules of the mean envelope operation. On this fundamental, the abnormal components exist in the over-sifting IMFs are extracted out, and the conclusion is testified with the infinite sifting experiment.

Empirical Mode Decomposition on Geometry Signal Based Spherical Parameterization

2014 ◽  
Vol 998-999 ◽  
pp. 860-863
Author(s):  
Jian Guo Wang ◽  
Qun E ◽  
Ke Ming Yao ◽  
Xin Long Wan
Keyword(s):  
Empirical Mode Decomposition ◽  
Main Idea ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Spherical Parameterization ◽  
Novel Method ◽  
Key Steps

A novel method based onEmpirical Mode Decomposition(EMD) is approached to process the geometry signal. The main idea is to decompose the signal into some different detail components called Intrinsic Mode Function (IMF). The key steps are as follows: First, the signal is spherical parameterization; Second it is transformed into the plane signal and sampled regularly; Third, the translated signal is processed as an image using Bid-Empirical Mode Decomposition, getting several image IMFs; Finally, invert mapping these IMFs to geometry signal and getting the geometry signal’s IMFs.We demonstrate the power of the algorithms through a number of application examples including de-noising and enhancement.

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

2014 ◽  
Vol 229 (9) ◽  
pp. 1630-1638 ◽  
Author(s):  
Jian-hua Cai
Keyword(s):  
Power Spectrum ◽  
Order Statistics ◽  
Empirical Mode Decomposition ◽  
Gaussian Noise ◽  
Rolling Bearing ◽  
Higher Order ◽  
Intrinsic Mode Function ◽  
Order Spectrum ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

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