scholarly journals Research on Roller Status Diagnosis of CRDM Based on SWT and HHT

2021 ◽  
Vol 252 ◽  
pp. 01053
Author(s):  
Liming Zhang ◽  
Lei Rong ◽  
Meng Jiao ◽  
Ling Li ◽  
Yaohua Yu
Keyword(s):  
Vibration Signal ◽  
Threshold Method ◽  
Transform Method ◽  
Control Rod ◽  
Drive Mechanism ◽  
Noise Interference ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Actual Equipment ◽  
The Status

For the control rod drive mechanism roller vibration signal’s characteristics of nonlinear and nonstationary. Based on actual equipment life experiment and roller fault experiment, a status diagnosis method of roller of control rod drive mechanism based on Semi-soft wavelet threshold and Hilbert-Huang transform is proposed. Firstly, semi-soft wavelet threshold method is used to reduce noise interference and the influence of end-point effect on empirical mode decomposition, and the improved Hilbert transform method is used to extract the fault characteristics of roller vibration signal. The experimental results show that the method can effectively eliminate the interference of noises and realize the status diagnosis of the roller.

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.

Gearbox Fault Detection Using Empirical Mode Decomposition

2004 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Original Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Envelope Spectrum

Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.

scholarly journals Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez Báez ◽  
José Mario Estévez Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Main Tool ◽  
Good Alternative ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Mean Differences

AbstractThe fast Fourier transform (FFT), has been the main tool for the EEG spectral analysis (SPA). However, as the EEG dynamics shows nonlinear and non-stationary behavior, results using the FFT approach may result meaningless. A novel method has been developed for the analysis of nonlinear and non-stationary signals known as the Hilbert-Huang transform method. In this study we describe and compare the spectral analyses of the EEG using the traditional FFT approach with those calculated with the Hilbert marginal spectra (HMS) after decomposition of the EEG with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-seconds EEG recorded from 19 leads of 47 healthy volunteers were studied. Although the spectral indices calculated for the explored EEG bands showed significant statistical differences for different leads and bands, a detailed analysis showed that for practical purposes both methods performed substantially similar. The HMS showed a reduction of the alpha activity (−5.64%), with increment in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, and also an increment in the theta band (+2.14%), and in the delta (+0.45%) band, and vice versa for the FFT method. For the weighted mean frequencies insignificant mean differences (lower than 1Hz) were observed between both methods for the delta, theta, alpha, beta-1 and beta-2 bands, and only for the gamma band values for the HMS were 3 Hz higher than with the FFT method. The HMS may be considered a good alternative for the SPA of the EEG when nonlinearity or non-stationarity may be present.

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.

Fault Feature Extraction of Helicopter Rotor System Based on HHT Envelope Demodulation

2013 ◽  
Vol 765-767 ◽  
pp. 2817-2821
Author(s):  
Chen Lu ◽  
Xiao Wei Du ◽  
Hong Mei Liu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rotor System ◽  
Helicopter Rotor ◽  
Center Frequency ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Redundancy Design ◽  
Fault Feature Extraction

Helicopter rotor system (HRS), which is a key component without redundancy design, is of significant importance for flight safety. Working under demanding environment, HRS faults are hard to detect. This paper proposes a new approach based on Hilbert-Huang Transform (HHT) and envelope demodulation to realize HRS fault feature extraction under strong interference. Empirical mode decomposition (EMD) was used to decompose the vibration signal into several intrinsic mode functions (IMFs) first, then, Hilbert transformation was applied to the IMFs to get the envelopes. And at last, fast Fourier transform (FFT) was adopted with the IMF which was closely related to the fault features. This method can avoid the selection of center frequency and filter band in resonance demodulation method, therefore, it has good adaptivity. Two commonly occurring faults in HRS are simulated on a test rig to validate the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method based on HHT envelope demodulation is effective for the HRS fault feature extraction.

Rotor Local Rubbing Fault Feature Analysis Based on Teager-Huang Transform

2014 ◽  
Vol 989-994 ◽  
pp. 3244-3247 ◽  
Author(s):  
Shang Kun Liu ◽  
Gui Ji Tang ◽  
Bin Pang
Keyword(s):  
Energy Operator ◽  
Vibration Signal ◽  
Analysis Method ◽  
Test Rig ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Teager Energy Operator ◽  
Mode Function ◽  
Mode Decomposition ◽  
Teager Energy

An analysis method based on Teager-Huang transform for rotor local rubbing fault diagnosis is introduced. Firstly, the original vibration signal is decomposed into some Intrinsic Mode Function (IMF) components by using Empirical Mode Decomposition (EMD) approach, secondly, Teager energy operator is applied to estimate the instantaneous amplitude and instantaneous frequency of each IMF component, so the time-frequency distribution of the signal is obtained. The rotor local rubbing fault is simulated on a rotor test rig. The analysis results show that this method compared with Hilbert-Huang transform (HHT) can track the occurrence of rotor local rubbing fault better and can extract the characteristics of rotor local rubbing fault effectively. It provides a reliable method for timely and accurate diagnostic to the rotor local rubbing fault.

Vibration Signal Analysis and Fault Diagnosis of Gears Based on Hilbert Marginal Spectrum

2014 ◽  
Vol 909 ◽  
pp. 121-126 ◽  
Author(s):  
Jiang Ping Wang ◽  
Jin Cui
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Characteristic Time Scale ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Vibration Signal Analysis ◽  
Non Stationary Signal

Hilbert-Huang transform is a new method of signal processing, which is very suitable for dealing with nonlinear and non-stationary signal. In this article, a gear fault diagnosis method based on Hilbert marginal spectrum is proposed in view of the non-stationary characteristics of gear vibration signal. First the original vibration signal is decomposed into several intrinsic mode functions (IMF) of different characteristic time scale smoothly by means of empirical mode decomposition (EMD) method. Then the Hilbert-Huang transform is carried out for IMF and the Hilbert marginal spectrum under different operating conditions are obtained. Gear faults can be judged through the analysis of the marginal spectrum. The experimental results show that this method can effectively diagnose the gear faults.

Application of Hilbert-Huang Transform Method on Fault Diagnosis for Wind Turbine Rotor

2009 ◽  
Vol 413-414 ◽  
pp. 159-166
Author(s):  
Qian Huang ◽  
Dong Xiang Jiang ◽  
Liang You Hong
Keyword(s):  
Wind Turbine ◽  
Original Data ◽  
Turbine Rotor ◽  
Noticeable Effect ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Wind Turbine Rotor

Many signals of wind turbine faults are non-stationary and have highly complex time-frequency characteristics. Traditional time-frequency analysis method, such as Windowed Fourier Transform method, has no noticeable effect in handing non-stationary signals. Hilbert-Huang Transform (HHT) is a new signal processing method for analyzing the non-stationary mechanical signals. Based on Empirical Mode Decomposition (EMD), the Intrinsic Mode Function (IMF) in HHT can reflect the intrinsic physical characteristics of original data. Moreover, it is a good way to identify the faults involving a breakdown change. First, the principles and advantages of the HHT are presented in detail in this paper. Then, three typical faults of wind turbine rotor, such as rotor imbalance, aerodynamic asymmetry due to blade surface roughness and yaw misalignment are discussed by the HHT. Last, reasonable conclusions are drawn by the comparison between this method and the Wavelet Transform (WT) method with the help of simulation fault signals. The results show the effectiveness of HHT method for diagnosing those faults of wind turbine rotor.

HHT-based Detection Method of Cutter Abnormal Vibration in Spiral Surface Machining

2019 ◽  
Vol 12 ◽  
Author(s):  
Xin Li ◽  
Yuliang Zhang ◽  
Jianping Yu ◽  
Xiaolei Deng
Keyword(s):  
Detection Method ◽  
Vibration Signal ◽  
Machining Process ◽  
Surface Machining ◽  
Hilbert Huang Transform ◽  
Vibration Detection ◽  
Mode Decomposition ◽  
Spiral Surface ◽  
The Imf

Background: Cutter abnormal vibration occurs frequently during the spiral surface machining process, and it results in low quality of finished surface. In order to suppress cutter abnormal vibration effectively, it is necessary to detect abnormal vibration as soon as possible, but the analysis and processing of the cutter abnormal vibration signal in spiral surface machining are difficult because of its complicated components and non-linear non-stationary characteristics. In this paper, a detection method of abnormal vibration signal based on empirical mode decomposition (EMD) and Hilbert–Huang transform (HHT) is proposed to be applied in spiral surface machining. Method: First, EMD of the cutter vibration signal in the spiral surface machining is performed to obtain a series of intrinsic mode function (IMF) components in different frequency bands. Then, the variation in the energy of each IMF component in the frequency domain and the correlation with the original signal are analyzed to obtain the IMF component with the largest amount of information on abnormal vibration symptom. Finally, Hilbert transform is conducted on the IMF component to extract the symptom features of abnormal vibration. Results: Experimental results show that the EMD–HHT based method to analyze the cutter vibration signal in the spiral surface machining can extract the symptom of abnormal vibration quickly and effectively and detect the abnormal vibration of the cutter rapidly. Conclusion: The proposed method based on HHT in this paper could be successfully used in abnormal vibration detection, and which could also provide basis and guarantee for the subsequent suppression of abnormal vibration.

Application of Hilbert-Huang Transform to Vibration Signal Analysis of Coal and Gangue

2010 ◽  
Vol 40-41 ◽  
pp. 995-999 ◽  
Author(s):  
Wei Liu
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Frequency Interval ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
The Difference ◽  
Vibration Signal Analysis

In this paper, a new method of vibration signal analysis of coal and gangue based on Hilbert-Huang transform is presented. Empirical mode decomposition algorithm was used to decompose the original vibration signal of coal and gangue into the intrinsic modes for further extract useful information contained in response signals under complicated environment. By analyzing local Hilbert marginal spectrum and local energy spectrum of the first four intrinsic mode function components, we found the difference of coal and rock in specific frequency interval that the amplitude and energy mainly distributed at frequency interval between 100Hz and 600Hz when coal was drawn, while the amplitude and energy were more concentrated at 1000Hz or so when gangue was drawn. Furthermore, the further analysis result from marginal spectrum of each intrinsic mode function component agreed well with the conclusion above. So the extracted features with the propose approach can be served as coal and gangue interface recognition.

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