scholarly journals Monitoring Machines by Using a Hybrid Method Combining MED, EMD, and TKEO

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mourad Kedadouche ◽  
Marc Thomas ◽  
Antoine Tahan
Keyword(s):  
Spectrum Analysis ◽  
Signal Analysis ◽  
Energy Operator ◽  
Analysis Tool ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Envelope Spectrum

Amplitude demodulation is a key for diagnosing bearing faults. The quality of the demodulation determines the efficiency of the spectrum analysis in detecting the defect. A signal analysis technique based on minimum entropy deconvolution (MED), empirical mode decomposition (EMD), and Teager Kaiser energy operator (TKEO) is presented. The proposed method consists in enhancing the signal by using MED, decomposing the signal in intrinsic mode functions (IMFs) and selects only the IMF which presents the highest correlation coefficient with the original signal. In this study the first IMF1 was automatically selected, since it represents the contribution of high frequencies which are first excited at the early stages of degradation. After that, TKEO is used to track the modulation energy. The spectrum is applied to the instantaneous amplitude. Therefore, the character of the bearing faults can be recognized according to the envelope spectrum. The simulation and experimental results show that an envelope spectrum analysis based on MED-EMD and TKEO provides a reliable signal analysis tool. The experimental application has been developed on acoustic emission and vibration signals recorded for bearing fault detection.

Acoustic Signals Analysis Based on Empirical Mode Decomposition and Spectrum Analysis Technique

2010 ◽  
Vol 40-41 ◽  
pp. 91-95 ◽  
Author(s):  
Yan Li Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Acoustic Signals ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Marginal Spectrum ◽  
Rock Interface ◽  
Coal Rock ◽  
Instantaneous Energy

A method to analyze the acoustic signals collected in fully-mechanized caving face is presented in this paper. Through analyzing the marginal spectrum and frequency spectrum of intrinsic mode functions obtained by empirical mode decomposition, acoustic signals’ frequency and amplitude characteristics are gotten, that is, high frequency signals about 1000Hz ~2800Hz are produced when the top coal is combined with gangue. Furthermore, the acoustic signals’ instantaneous energy spectrums in the frequency range of 1000Hz ~2800Hz can be used to identify the coal-rock interface.

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.

Quantifying the complexity of human colonic pressure signals using an entropy measure

2016 ◽  
Vol 61 (1) ◽  
pp. 127-132 ◽  
Author(s):  
Fei Xu ◽  
Guozheng Yan ◽  
Kai Zhao ◽  
Li Lu ◽  
Zhiwu Wang ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Colonic Motility ◽  
Approximate Entropy ◽  
Entropy Measure ◽  
Intrinsic Mode Functions ◽  
Spectral Entropy ◽  
Mode Decomposition ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Non Stationary Signal

Abstract Studying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

scholarly journals A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4751 ◽  
Author(s):  
Xiaoling Li ◽  
Bin Liu ◽  
Yang Liu ◽  
Jiawei Li ◽  
Jiarui Lai ◽  
...  
Keyword(s):  
Signal Detection ◽  
Spectrum Analysis ◽  
Doppler Radar ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Signal Separation ◽  
Radar Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

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.

New Feature Extraction Method for the Detection of Defects in Rolling Element Bearings

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Ling Xiang ◽  
Aijun Hu
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Outer Race ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Envelope Spectrum ◽  
Detection Of Defects

This paper proposes a new method based on ensemble empirical mode decomposition (EEMD) and kurtosis criterion for the detection of defects in rolling element bearings. Some intrinsic mode functions (IMFs) are presented to obtain symptom wave by EEMD. The different kurtosis of the intrinsic mode function is determined to select the envelope spectrum. The fault feature based on the IMF envelope spectrum whose kurtosis is the maximum is extracted, and fault patterns of roller bearings can be effectively differentiated. Practical examples of diagnosis for a rolling element bearing are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race and inner-race, can be effectively identified by the proposed method.

Pipeline Leak Detection Based on Acoustic Emission Using Empirical Mode Decomposition Method

2008 ◽  
Author(s):  
Yibo Li ◽  
Junlin Li ◽  
Liying Sun ◽  
Shijiu Jin ◽  
Shenghua Han
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Signal Analysis ◽  
Leak Detection ◽  
Detection Sensitivity ◽  
Intrinsic Mode Functions ◽  
Location Accuracy ◽  
Noise Interference ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

Corrosion in pipeline is a significant problem in the oil industry and there is also much interest in reducing leak due to corrosion. Correlation techniques are widely used in leak detection, and these have been extremely effective when attempting to locate leaks in metal pipes. Acoustic emission is a new non-destructive pipeline inspection technology which can be used to monitor crucial part of pipelines and detect pipe corrosion or leak in real time. However, AE signals causing by corrosion and leak are liable to noise interference on field. Aiming at solving the noise interference problems and increase the detection sensitivity and location accuracy of the leak, advanced signal analysis method based on Empirical Mode Decomposition were researched. Empirical Mode Decomposition is a great breakthrough in non-stable signal analysis and it decomposes the signals into a sum of finite intrinsic mode functions (IMF), which have real physical meaning. In the experiment, the leak signals from a 30 m pipeline were decomposed into 9 intrinsic mode functions by EMD, among which some IMF components containing typical AE characteristic can be selected to reconstruct the signal, and thus intrinsic characteristic of leak signal could be extracted and noise interference would be eliminated. Location accuracy of the leaking hole calculated with the reconstructed signals based on EMD algorithm was increased 64%.

EMD and Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 233-237 ◽  
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Oscillation Modes ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Envelope Spectrum

A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.

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.

Kick Detection Using Downhole Accelerometer Data

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Robello Samuel
Keyword(s):  
High Frequency ◽  
Damping Ratio ◽  
Energy Operator ◽  
Damping Factor ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Minimum Entropy ◽  
Time Data ◽  
Essential Information ◽  
Vibration Data

Abstract The high-frequency downhole vibration data include a greater amount of hidden information than the low-frequency surface data. This paper proposes the monitoring of high-frequency acceleration data for early kick detection. The trend of accelerator sensor values is monitored, rather than processed. When the gas, fluid, or oil kick occurs, the fluid influx reduces the viscosity of the fluid in annulus, which causes the degradation of the damping factor. The sensor installed on the drillpipe detects the velocity/acceleration change that results in the damping factor change. This approach includes an analytical model to calculate the effect of the damping ratio on the acceleration calculations. The fluid influx and migration in the wellbore strongly affect the damping factor. The paper presents a method of deconvoluting the sensor values that uses a combination of minimum entropy deconvolution and Teager-Kaiser energy operator to remove the noise, unwanted sensor values, and likelihood of false prediction. It is then proposed to calculate instantaneous jerk and jerk intensity at each depth. The trend of the final intrinsic mode functions (IMF) at each depth is continuously monitored to predict the formation influx, if any. A novel concept of monitoring the incremental IMF and IMF energy at each depth is introduced. This technique is shown to reveal a wealth of information and simplifies the process of monitoring and analyzing the vast amount of available data. The methodology developed is applied to extract the essential information from high-frequency vibration data to make real-time data monitoring straightforward, reliable, and fast.

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