scholarly journals Pulsation Signals Analysis of Turbocharger Turbine Blades based on Optimal EEMD and TEO

2019 ◽  
Vol 26 (3) ◽  
pp. 78-86
Author(s):  
Fengli Wang
Keyword(s):  
Turbine Blades ◽  
Energy Operator ◽  
Vibration Signal ◽  
Optimization Method ◽  
Ensemble Empirical Mode Decomposition ◽  
Significant Feature ◽  
Intrinsic Mode Functions ◽  
Blade Vibration ◽  
Mode Decomposition ◽  
Nonlinear Signal

Abstract Turbocharger turbine blades suffer from periodic vibration and flow induced excitation. The blade vibration signal is a typical non-stationary and sometimes nonlinear signal that is often encountered in turbomachinery research and development. An example of such signal is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient mode in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on optimal parameters of Ensemble Empirical Mode Decomposition (EEMD) and Teager Energy Operator (TEO) is proposed. First, an optimization method was designed for adaptive determining appropriate EEMD parameters for the measured vibration signal, so that the significant feature components can be extracted from the pulsating signals. Then Correlation Kurtosis (CK) is employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method.

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.

scholarly journals Gear Fault Detection Based on Teager-Huang Transform

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Hui Li ◽  
Haiqi Zheng ◽  
Liwei Tang
Keyword(s):  
Fault Detection ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Novel Method ◽  
Detection And Diagnosis

Gear fault detection based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique is presented. This novel method is named as Teager-Huang transform (THT). EMD can adaptively decompose the vibration signal into a series of zero mean Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the Intrinsic Mode Functions at any instant. The experimental results provide effective evidence that Teager-Huang transform has better resolution than that of Hilbert-Huang transform. The Teager-Huang transform can effectively diagnose the fault of the gear, thus providing a viable processing tool for gearbox defect detection and diagnosis.

scholarly journals Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

2020 ◽  
Vol 10 (15) ◽  
pp. 5078
Author(s):  
Wenxiao Guo ◽  
Ruiqin Li ◽  
Yanfei Kou ◽  
Jianwei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Fault Simulation ◽  
Random Noise ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Mining Machinery

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

The Fault Pattern Identification of Hydraulic Pump Based on the Sensitive Component STSA

2014 ◽  
Vol 635-637 ◽  
pp. 790-794
Author(s):  
Yu Kui Wang ◽  
Hong Ru Li ◽  
Peng Ye
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hydraulic Pump ◽  
Mode Decomposition ◽  
Novel Method ◽  
Symbolic Time Series Analysis

A novel method which is based on ensemble empirical mode decomposition (EEMD) and symbolic time series analysis (STSA) was proposed in this paper. Firstly, the vibration signal of hydraulic pump was decomposed into a number of stationary intrinsic mode functions (IMFs). Secondly, the sensitive component was extracted. Finally, the relative entropy (RE) was extracted from the sensitive components and they were used as the indicator to distinguish the faults of hydraulic pump. The research results of actual testing vibration signal demonstrated the rationality and effectiveness of the proposed method in this paper.

Quadratic Hilbert Transform Demodulation Based on Time-Delayed Correlation Treatment and EEMD

2013 ◽  
Vol 765-767 ◽  
pp. 2715-2719 ◽  
Author(s):  
Qing Xiong ◽  
Wei Hua Zhang ◽  
Gui Ming Mei
Keyword(s):  
Hilbert Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Unbiased Estimation ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Bearing Defect ◽  
Marginal Spectrum ◽  
Repeated Analysis

To deal with the demodulation problem of rolling bearing defect vibration signal in heavy noise, a new method based on time-delayed correlation algorithm and ensemble empirical mode decomposition (EEMD) is presented. Introduced the time-delayed autocorrelation de-noising principle. After the discretization and unbiased estimation of the original signals autocorrelation function , de-noising pretreatment is implemented by appending a rectangle window. Then an envelope signal can be obtained by the first Hilbert transform. After the EEMD decomposition, some interested intrinsic mode functions (IMFs) can be collected. By making the second Hilbert transform of the IMFs, we can get the local Hilbert marginal spectrum from which the defects in a rolling bearing can be identified. By repeated analysis of simulation signals and actual rolling bearings defect vibration signal, the results show that the proposed method is more effective than direct modulation or only time-delayed correlation demodulation or combine time-delayed correlation with EMD demodulation in de-noising and diagnosing the rolling bearing's defect information.

scholarly journals EEMD-MUSIC-Based Analysis for Natural Frequencies Identification of Structures Using Artificial and Natural Excitations

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
David Camarena-Martinez ◽  
Juan P. Amezquita-Sanchez ◽  
Martin Valtierra-Rodriguez ◽  
Rene J. Romero-Troncoso ◽  
Roque A. Osornio-Rios ◽  
...  
Keyword(s):  
Natural Frequencies ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Operating Conditions ◽  
Ambient Vibration ◽  
Intrinsic Mode Functions ◽  
Highway Traffic ◽  
Vibration Signals ◽  
Multiple Signal Classification ◽  
Mode Decomposition

This paper presents a new EEMD-MUSIC- (ensemble empirical mode decomposition-multiple signal classification-) based methodology to identify modal frequencies in structures ranging from free and ambient vibration signals produced by artificial and natural excitations and also considering several factors as nonstationary effects, close modal frequencies, and noisy environments, which are common situations where several techniques reported in literature fail. The EEMD and MUSIC methods are used to decompose the vibration signal into a set of IMFs (intrinsic mode functions) and to identify the natural frequencies of a structure, respectively. The effectiveness of the proposed methodology has been validated and tested with synthetic signals and under real operating conditions. The experiments are focused on extracting the natural frequencies of a truss-type scaled structure and of a bridge used for both highway traffic and pedestrians. Results show the proposed methodology as a suitable solution for natural frequencies identification of structures from free and ambient vibration signals.

scholarly journals Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 209 ◽  
Author(s):  
Shaohua Xue ◽  
Jianping Tan ◽  
Lixiang Shi ◽  
Jiwei Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Sensitivity Index ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
New Perspective

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

Data Processing of Pulsation Signals of a Turbine Based on EMD and Teager Energy Operator

2018 ◽  
Author(s):  
Fengli Wang ◽  
Hua Chen ◽  
Aiguo Gu ◽  
Wei Hu
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Operator ◽  
Intrinsic Mode Functions ◽  
Blade Vibration ◽  
Teager Energy Operator ◽  
Mode Decomposition ◽  
Novel Method ◽  
Eemd Method ◽  
Teager Energy ◽  
Strain Signal

Nonstationary and nonlinear signals are often encountered in turbomachinery research and development. Sometimes the frequency of these signals changes with time. One such an example is the pulsating pressure and strain signals measured during engine ramp to find the maximum resonance strain or during engine transient in applications. As the pulsation signals can come from different disturbance sources, detecting the weak useful signals under a noise background can be difficult. For this type of signals, a novel method based on Empirical Mode Decomposition (EMD) and Teager Energy Operator (TEO) is proposed. First, the signals are processed by a self-adaptive Lifting Wavelet Transform (LWT) to remove noises and enhance the Signal to Noise Ratio (SNR). Then the EMD and Correlation Kurtosis (CK) are employed to select the sensitive Intrinsic Mode Functions (IMFs). In the end, TEO algorithm is applied to the selected sensitive IMF to identify the characteristic frequencies. A case of measured sound signal and strain signal from a turbocharger turbine blade was studied to demonstrate the capabilities of the proposed method. In this case the FFT failed to identify the blade vibratory signal at all, and the EEMD method was barely able to do so. The proposed method successfully captured the blade vibration from the both sound and strain signals.

Research on Rolling Element Bearing Fault Diagnosis Based on EEMD and Correlated Kurtosis

2014 ◽  
Vol 680 ◽  
pp. 198-205 ◽  
Author(s):  
Xiao Lin Wang ◽  
Wei Hua Han ◽  
Han Gu ◽  
Cun Hu ◽  
Xing Xing Han
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition

In order to extract the faint fault information from complicated vibration signal of bearing, the correlated kurtosis is introduced into the field of rolling bearing fault diagnosis. Combined with ensemble empirical mode decomposition (EEMD) and correlated kurtosis, a feature extraction method is proposed. According to the method, by EEMD processing a group of intrinsic mode functions (IMFs) are obtained, then the IMF with maximal correlated kurtosis is selected, and the weak fault signal is clearly extracted. The effectiveness of the method is demonstrated on both simulated signal and actual data.

scholarly journals Research of Fault Diagnosis Based on Sensitive Intrinsic Mode Function Selection of EEMD and Adaptive Stochastic Resonance

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Zhixing Li ◽  
Boqiang Shi
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Ensemble Empirical Mode Decomposition ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Selection Of

A novel methodology for the fault diagnosis of rolling bearing in strong background noise, based on sensitive intrinsic mode functions (IMFs) selection of ensemble empirical mode decomposition (EEMD) and adaptive stochastic resonance, is proposed. The original vibration signal is decomposed into a group of IMFs and a residual trend item by EEMD. Constructing weighted kurtosis index difference spectrum (WKIDS) to adaptively select sensitive IMFs, this method can overcome the shortcomings of the existing methods such as subjective choice or need to determine a threshold using the correlation coefficient. To further reduce noise and enhance weak characteristics, the adaptive stochastic resonance is employed to amplify each sensitive IMF. Then, the ensemble average is used to eliminate the stochastic noise. The simulation and rolling element bearing experiment with an inner fault are performed to validate the proposed method. The results show that the proposed method not only overcomes the difficulty of choosing sensitive IMFs, but also, combined with adaptive stochastic resonance, can better enhance the weak fault characteristics. Moreover, the proposed method is better than EEMD and adaptive stochastic resonance of each sensitive IMF, demonstrating the feasibility of the proposed method in highly noisy environments.

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