Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum

2011 ◽  
Vol 142 ◽  
pp. 3-6
Author(s):  
Jin Ming Lu ◽  
Fan Lin Meng ◽  
Hua Shen ◽  
Li Bing Ding ◽  
Su Nin Bao
Keyword(s):  
Energy Density ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Density Spectrum ◽  
Hilbert Spectrum ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
The Common ◽  
Instantaneous Energy

A very short impulse energy called ‘impulsion energy’ can be produced when the gear meshing with gear pitting fault and excited the resonance of the structure. The common techniques have inconvenience to deal with this vibration signal. A new fault diagnosis method based on EEMD and instantaneous energy density spectrum is proposed here. The IMFs generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The characteristic frequencies were found in the instantaneous energy density of Hilbert spectrum. The effectiveness of this method was demonstrated by analysis the vibration signals of a gear with pitting fault.

Fault Diagnoise of Rolling Bearing Based on EEMD and Instantaneous Energy Density Spectrum

2011 ◽  
Vol 97-98 ◽  
pp. 741-744
Author(s):  
Jin Ming Lu ◽  
Fan Lin Meng ◽  
Hua Shen ◽  
Li Bing Ding ◽  
Su Nin Bao
Keyword(s):  
Energy Density ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Density Spectrum ◽  
Hilbert Spectrum ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Inner Race ◽  
Instantaneous Energy

A new fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) and instantaneous energy density spectrum is proposed here. The intrinsic mode functions (IMFs) generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The characteristic frequencies were found in the instantaneous energy density of Hilbert spectrum. The effectiveness of this method was demonstrated by analysis the vibration signals of a rolling bearing with inner-race fault.

Denoising of the Base Vibration Signal Based on EEMD

2011 ◽  
Vol 143-144 ◽  
pp. 689-693 ◽  
Author(s):  
X.J. Li ◽  
K. Wang ◽  
G.B. Wang ◽  
Q. Li
Keyword(s):  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Decomposition Level ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Common ◽  
Eemd Method ◽  
Selection Of ◽  
Better Than

Vibration signals of rotating machinery on the base are very weak and always buried in noisy noise; the common denoising methods have become powerless. It presents an ensemble empirical mode decomposition method (EEMD) that is used to denoise for the base vibration signal, which not only to overcome the problem of mode mixing, but also to avoid the selection of wavelet basis function and decomposition level of the problem. Experimental results of simulation and measured data show that EEMD method can effectively reduce the base vibration signal noise, which is better than the wavelet and EMD denoising method.

Diagnosis of Ball Screw Preload Loss by Vibration Signals through the Hilbert-Huang Transform and Multiscale Measure

2011 ◽  
Vol 58-60 ◽  
pp. 636-641
Author(s):  
Yan Chen Shin ◽  
Yi Cheng Huang ◽  
Jen Ai Chao
Keyword(s):  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ball Screw ◽  
Multiscale Entropy ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Diagnosis Method

This paper proposes a diagnosis method of ball screw preload loss through the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process when machine tool is in operation. Maximum dynamic preload of 2% and 4% ball screws are predesigned, manufactured and conducted experimentally. Vibration signal patterns are examined and revealed by Empirical Mode Decomposition (EMD) with Hilbert Spectrum. Different preload features are extracted and discriminated by using HHT. The irregularity development of ball screw with preload loss is determined and abstracting via MSE based on complexity perception. The experiment results successfully show preload loss can be envisaged by the proposed methodology.

scholarly journals Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Chen ◽  
Xin-Cong Zhou ◽  
Jun-Qiang Fang ◽  
Peng-fei Zheng ◽  
Jun Wang
Keyword(s):  
Fault Diagnosis ◽  
Original Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Coupling System ◽  
Reconstructed Signal ◽  
Gear Transmission System ◽  
Gear Fault ◽  
Diagnosis Method

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

2013 ◽  
Vol 694-697 ◽  
pp. 1151-1154
Author(s):  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Jia Xing Zhu ◽  
Yan Ping Su
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

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.

Fault Diagnosis for Misfire and Abnormal Clearance in a Diesel Engine Based on EEMD

2011 ◽  
Vol 97-98 ◽  
pp. 702-705
Author(s):  
Jin Ming Lu ◽  
Fan Lin Meng ◽  
Hua Shen ◽  
Li Bin Ding ◽  
Jie Ma
Keyword(s):  
Fault Diagnosis ◽  
Energy Density ◽  
Valve Train ◽  
Intake Valve ◽  
Density Spectrum ◽  
Vibration Signals ◽  
Diagnosis Method ◽  
Diesel Cylinder ◽  
Instantaneous Energy ◽  
Mode Mixing

The misfire of one or more diesel cylinder and the abnormal clearance in the intake valve train of cylinder are common faults which affect the safety and the performance of the engine seriously. A new fault diagnosis method based on EEMD and instantaneous energy density spectrum is proposed here. The IMFs generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The instantaneous energy density of these IMFs can distinguish the faulty impacts clearly. The effectiveness of this method was demonstrated by analysis the vibration signals of misfire fault and abnormal clearance in the intake valve train of 3110 diesel.

Application of MSE-EEMD Method in Gear Fault Diagnosis

2014 ◽  
Vol 530-531 ◽  
pp. 261-265
Author(s):  
Min Qiang Xu ◽  
Yong Bo Li ◽  
Hai Yang Zhao ◽  
Si Yang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Engineering Application ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Multiscale Entropy ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Eemd Method

Focus on the nonlinear and non-stationary characteristics of gear box vibration signal, the method of gear fault diagnosis based on Ensemble Empirical Mode Decomposition (EEMD) and multiscale entropy (MSE) was proposed . The complicated signal can be decomposed into several stationary IMF components with reality meanings by EEMD which has the advantages of eliminating aliasing state of vibration signal, and the MSE can extract the fault feature from the signals effectively. The concepts of EEMD and MSE are introduced firstly, and then they are applied to measure the complexity of gearbox signals. Through the engineering application of the diagnosis on gear typical fault of different wearing degree demonstrated that the proposed method can extracting the fault feature of gear fault effectively and realize the gear fault diagnosis.

scholarly journals Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 593 ◽  
Author(s):  
Guiji Tang ◽  
Bin Pang ◽  
Yuling He ◽  
Tian Tian
Keyword(s):  
Fault Diagnosis ◽  
Energy Density ◽  
Dynamic Time Warping ◽  
Vibration Signal ◽  
Time Warping ◽  
Efficient Operation ◽  
Fault Features ◽  
Diagnosis Method ◽  
Dynamic Time ◽  
Instantaneous Energy

The accurate fault diagnosis of gearboxes is of great significance for ensuring safe and efficient operation of rotating machinery. This paper develops a novel fault diagnosis method based on hierarchical instantaneous energy density dispersion entropy (HIEDDE) and dynamic time warping (DTW). Specifically, the instantaneous energy density (IED) analysis based on singular spectrum decomposition (SSD) and Hilbert transform (HT) is first applied to the vibration signal of gearbox to acquire the IED signal, which is designed to reinforce the fault feature of the signal. Then, the hierarchical dispersion entropy (HDE) algorithm developed in this paper is used to quantify the complexity of the IED signal to obtain the HIEDDE as fault features. Finally, the DTW algorithm is employed to recognize the fault types automatically. The validity of the two parts that make up the HIEDDE algorithm, i.e., the IED analysis for fault features enhancement and the HDE algorithm for quantifying the information of signals, is numerically verified. The proposed method recognizes the fault patterns of the experimental data of gearbox accurately and exhibits advantages over the existing methods such as multi-scale dispersion entropy (MDE) and refined composite MDE (RCMDE).

Sign in / Sign up

Export Citation Format

Share Document