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.

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.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

scholarly journals Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang
Keyword(s):  
Fault Diagnosis ◽  
Relative Entropy ◽  
Analog Circuits ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method ◽  
Learning Machine ◽  
Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

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.

A New Rotor Fault Diagnosis Method Based on EEMD Sample Entropy and Grey Relation Degree

2013 ◽  
Vol 347-350 ◽  
pp. 426-429 ◽  
Author(s):  
Wen Bin Zhang ◽  
Yan Jie Zhou ◽  
Jia Xing Zhu ◽  
Ya Song Pu
Keyword(s):  
Fault Diagnosis ◽  
Rank Order ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Sampled Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Grey Relation

In this paper, a new rotor fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by rank-order morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs). Thirdly, some IMFs containing the most dominant fault information were calculated the sample entropy for four rotor conditions. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in rotor fault diagnosis. Its suitable for on-line monitoring and diagnosis of rotating machinery.

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.

scholarly journals Fault Diagnosis of Rolling Bearing based on an Improved Denoising Technique using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

2021 ◽  
Author(s):  
Prashant Kumar Sahu ◽  
Rajiv Nandan Rai
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Adaptive Thresholding ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Envelope Spectrum

Abstract The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.

QUANTITATIVE NON-STATIONARY ASSESSMENT OF CEREBRAL HEMODYNAMICS BY EMPIRICAL MODE DECOMPOSITION OF CEREBRAL DOPPLER FLOW VELOCITY

2013 ◽  
Vol 05 (01) ◽  
pp. 1350002 ◽  
Author(s):  
CHIA-CHI CHANG ◽  
HUNG-YI HSU ◽  
TZU-CHIEN HSIAO
Keyword(s):  
Energy Density ◽  
Flow Velocity ◽  
Empirical Mode Decomposition ◽  
Cerebral Blood Flow Velocity ◽  
Ensemble Empirical Mode Decomposition ◽  
Cerebral Hemodynamics ◽  
Intrinsic Mode Functions ◽  
Dynamic Regulation ◽  
Arterial Blood ◽  
Mode Decomposition

Dynamic regulation of cerebral circulation involves complex interaction between cardiovascular, respiratory, and autonomic nervous systems. Evaluating cerebral hemodynamics by using traditional statistic- and linear-based methods would underestimate or miss important information. Complementary ensemble empirical mode decomposition (CEEMD) has great capability of adaptive feature extraction from non-linear and non-stationary data without distortion. This study applied CEEMD for assessment of cerebral hemodynamics in response to physiologic challenges including paced 6-cycle breathing, hyperventilation, 7% CO2 breathing and head-up tilting test in twelve healthy subjects. Intrinsic mode functions (IMFs) were extracted from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) signals, and was quantified by logarithmic averaged period and logarithmic energy density. The IMFs were able to show characteristics of ABP and CBFV waveform morphology in beat-to-beat timescale and in long-term trend scale. The changes in averaged period and energy density derived from IMFs were helpful for qualitative and quantitative assessment of ABP and CBFV responses to physiologic challenges. CEEMD is a promising method for assessing non-stationary components of systemic and cerebral hemodynamics.

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.

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.

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