The Application of Fault Diagnosis Based on EMD and Real Modulation Zoom Method

2013 ◽  
Vol 274 ◽  
pp. 37-40
Author(s):  
Jing Zhong Xiang ◽  
Fu Peng Ge ◽  
Han Sun ◽  
Xian Jiang Shi
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Hilbert Transformation ◽  
Frequency Resolution ◽  
Mode Decomposition ◽  
Shift Frequency ◽  
Envelope Spectrum ◽  
High Resonant Frequency

It is first to make Empirical Mode Decomposition (EMD) and achieve Hilbert-Hung transform (HHT) of envelope spectrum after Hilbert transformation, which can resolve the cross fault existing in Hilbert transformation is utilized merely. However, it can’t improve the resolution of envelope spectrum, which doesn’t adapt the characteristic of high resonant frequency of rolling bearing and low fault frequency. The paper utilizing the principle of real modulation shift frequency moves the EMD decomposition components of high frequency section to low frequency section and makes analysis of envelope spectrum after Hilbert transformation, which resolves the problem of low frequency resolution of pure HHT.

EMD and Zoom Envelope Spectrum for Rolling Bearing Fault Diagnosis

2013 ◽  
Vol 791-793 ◽  
pp. 841-844
Author(s):  
Jing Zhong Xiang ◽  
Hui Zhu ◽  
Meng Jin Gao ◽  
Fang Jian Chen ◽  
Xian Jiang Shi ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
High Frequency ◽  
Low Frequency ◽  
Practical Method ◽  
Rolling Bearing ◽  
Frequency Component ◽  
High Frequency Component ◽  
The Hilbert Transform ◽  
Envelope Spectrum ◽  
High Frequency Resonance

ntrinsic mode function component (IMF) has been effectively decomposed by using empirical mode (EMD) decomposition method, and the high-frequency resonance frequency band the rolling bearing components is extracted. Then the high frequency component of the IMF is shifted to the lower frequency by using real frequency modulation principle, and a new envelope refining spectrum is obtained through the Hilbert transform after low frequency sampling. The method not only simplifies the design of band-pass filtering process but improves refinement computational efficiency of the envelope spectrum. The method verifies the correctness of the theory and practical method through the analysis of the fault diagnosis of rolling bearing inner ring experiment.

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.

The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

2013 ◽  
Vol 300-301 ◽  
pp. 344-350 ◽  
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Jin Chuan Han
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Eemd Method

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

scholarly journals Compound Fault Diagnosis of Rolling Bearing Based on ACMD, Gini Index Fusion and AO-LSTM

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2386
Author(s):  
Jie Ma ◽  
Xinyu Wang
Keyword(s):  
Fault Diagnosis ◽  
Gini Index ◽  
Short Term Memory ◽  
Operation Mode ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Training Set ◽  
Test Set ◽  
Mode Decomposition ◽  
Envelope Spectrum

Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes it difficult to accurately identify all kinds of faults. For such problems, a compound fault diagnosis method based on adaptive chirp mode decomposition (ACMD), Gini index fusion and long short-term memory (LSTM) neural network optimized by Aquila Optimizer (AO) is proposed. Firstly, a series of IMF components are obtained by decomposing the vibration signal by means of ACMD, and the required components are selected by using the correlation coefficient method. Then, the Gini index of the square envelope (GISE) and the Gini index of the square envelope spectrum (GISES) of each component are calculated, respectively, and they are fused to construct a highly dimensional feature matrix. Then, with the aim of solving the problem of difficult selection of LSTM hyperparameters, the AO-LSTM model is constructed. Finally, the feature matrix is divided into a training set and a test set. The training set is input into the model for training, and then the training network is used to predict the test set, and outputs diagnostic results. The simulation and experimental results show that the proposed method can achieve higher accuracy and stronger robustness, compared with the existing intelligent diagnosis methods for bearing compound faults.

Analysis of Fault Diagnosis for Rolling Bearing Based on EMD and Local Smoothness Index

2012 ◽  
Vol 490-495 ◽  
pp. 2007-2011
Author(s):  
Ai Jun Hu ◽  
Yu Zhu ◽  
Xue Wang
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Bearing Fault Diagnosis ◽  
Local Smoothness ◽  
Mode Decomposition ◽  
Smoothness Index ◽  
Envelope Method ◽  
The Common ◽  
Envelope Spectrum

A new method based on EMD (empirical mode decomposition) and local smoothness index for rolling bearing fault diagnosis is proposed. With this method, the local smooth index of each IMF (intrinsic mode function) got by empirical mode decomposition is calculated, IMFs with smaller local smoothness index and smaller fluctuation of its smoothness index are selected to analysis with Hilbert envelope spectrum, and the method proposed overcomes blindness of choosing the IMFs with the common EMD envelope method. Factual fault signal of rolling bearing is analyzed; the fault frequency of the rolling bearing is identified accurately

Refinement Envelope Analysis for Small Failures of Rolling Bearing

2011 ◽  
Vol 383-390 ◽  
pp. 2622-2627
Author(s):  
Shu Shang Zhao ◽  
Juan Juan Pan
Keyword(s):  
Fault Diagnosis ◽  
Hilbert Transform ◽  
Vibration Spectrum ◽  
Rolling Bearing ◽  
Bearing Failure ◽  
Wavelet Filter ◽  
Bearing Defect ◽  
Small Fault ◽  
Bearing Vibration ◽  
Envelope Spectrum

In the rotating machinery, rolling bearing is used widespread in many places. Due to various reasons, there is great dispersion in the life of bearing. Therefore, it is very important to have fault diagnosis of rolling bearing, especially the small fault diagnosis of rolling bearing. According to the characteristics of rolling bearing defect signals and the features integrated with wavelet transform, Hilbert transform and envelope spectrum detailed analysis, this text proposed a method to judge the bearing failure. At first, bearing vibration signals are reconstructed from wavelet filter and envelope signals are obtained by Hilbert transform and then vibration spectrum is obtained from the refining envelope spectrum. Bearing failure is judged from the refining frequency spectrum. Bearing failure is also estimated by experiment to verify the correctness of theoretical analysis.

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

scholarly journals Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 694 ◽  
Author(s):  
Ruicheng Zhang ◽  
Chengfa Gao ◽  
Shuguo Pan ◽  
Rui Shang
Keyword(s):  
Real Time ◽  
High Precision ◽  
High Frequency ◽  
Spectral Response ◽  
Low Frequency ◽  
Deformation Monitoring ◽  
Full Time ◽  
Dynamic Displacement ◽  
Mode Decomposition ◽  
Acceleration Sensors

Real-time dynamic displacement and spectral response on the midspan of Jiangyin Bridge were calculated using Global Navigation Satellite System (GNSS) and a speedometer for the purpose of understanding the dynamic behavior and the temporal evolution of the bridge structure. Considering that the GNSS measurement noise is large and the velocity/acceleration sensors cannot measure the low-frequency displacement, the Variational Mode Decomposition (VMD) algorithm was used to extract the low-frequency displacement of GNSS. Then, the low-frequency displacement extracted from the GNSS time series and the high-frequency vibration calculated by speedometer were combined in this paper in order to obtain the high precision three-dimensional dynamic displacement of the bridge in real time. Simulation experiment and measured data show that the VMD algorithm could effectively resist the modal aliasing caused by noise and discontinuous signals compared with the commonly used Empirical Mode Decomposition (EMD) algorithm, which is guaranteed to get high-precision fusion data. Finally, the fused displacement results can identify high-frequency vibrations and low-frequency displacements of a mm level, which can be used to calculate the spectral characteristics of the bridge and provide reference to evaluate the dynamic and static loads, and the health status of the bridge in the full frequency domain and the full time domain.

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