Study of Start-Up Vibration Response for Oil Whip and Dry Whip Through Hilbert Huang Transform

2011 ◽  
Author(s):  
Chen-Chao Fan ◽  
Jhe-Wei Syu ◽  
Min-Chun Pan ◽  
Wen-Chang Tsao
Keyword(s):  
Energy Concentration ◽  
Full Spectrum ◽  
Constant Frequency ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Oil Whip ◽  
Fluid Handling ◽  
Mode Decomposition ◽  
Excited Vibration ◽  
Concentration Changes

Oil whip induces self-excited vibration in fluid-handling machines, and what is worse, it can cause self-excited reverse precessional full annular rub, known as “dry whip” which is a secondary phenomenon resulting from a primary cause and may lead to a catastrophic failure of machines; that is, “coincidence of oil whip and dry whip” that occurs repeatedly with constant frequency and amplitude in small clearance cases of fluid-handling machines. Early detection of rub malfunction is essential to avoid damage. Hilbert Huang Transform, which included an empirical mode decomposition and Hilbert spectral analysis, is applied. Hilbert Huang Transform is a great method for analyzing non-linear and non-stationary signals, such as rotor startup signals. Hilbert Huang Transform clearly indicates instability at its initiation stage, and energy concentration changes by different stages. Malfunctions like rub can not observe by Hilbert spectrum. Hilbert spectrum combining full spectrum is developed, as know full Hilbert spectrum, to interpret the rub. The coincidence of oil whip and dry whip is observed definitely through FHS. The advantage of the full Hilbert spectrum is to offer a faster, more efficient method to diagnose fluid-induced instability.

Active Elimination of Oil Whip and Dry Whip Through the Use of Electromagnetic Exciter

2010 ◽  
Author(s):  
Chen-Chao Fan ◽  
Wen-Chang Tsao ◽  
Min-Chun Pan ◽  
Jhe-Wei Syu
Keyword(s):  
Root Locus ◽  
Constant Frequency ◽  
Motion Equations ◽  
Rotary Machine ◽  
Oil Whip ◽  
Fluid Handling ◽  
Excited Vibration ◽  
Small Clearance ◽  
Secondary Phenomenon ◽  
Operational Range

Oil whip induces self-excited vibration in fluid-handling machines, and what is worse, it can cause self-excited reverse precessional full annular rub, known as “dry whip”, which is a secondary phenomenon resulting from a primary cause, and may lead to a catastrophic failure of machines; that is, “coincidence of oil whip and dry whip” that occurs repeatedly with constant frequency and amplitude in small clearance cases. This paper presents a technique using an electromagnetic exciter (EE), that can raise the threshold of stability of a rotary machine and then eliminate the coincidence of oil whip and dry whip in the normal operational range. In addition, an experimental rotor rig, a general model of a rotor/beating system with an EE and corresponding governing motion equations, computation of stiffness supplied by the EE, design example, stability judged by root locus plots, and criterion for eliminating the coincidence of the oil whip and dry whip are constructed, derived and established. Experimental results demonstrate that the coincidence of oil whip and dry whip of the rotary machine can be removed effectively. These results can be used to diagnose rub and fluid-induced instability in this kind of rotary machine.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

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.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

scholarly journals An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach

2016 ◽  
Vol 44 ◽  
pp. 141-150
Author(s):  
Kazi Mahmudul Hassan ◽  
Md. Ekramul Hamid ◽  
Takayoshi Nakai
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Separation ◽  
Audio Signal ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Separation Result ◽  
Mode Decomposition ◽  
Recent Approach ◽  
Non Stationary Signal

This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.

HILBERT–HUANG TRANSFORM FOR FEATURE EXTRACTION OF TEMPERATURE MODULATED MOS SENSORS

2013 ◽  
Vol 09 (03n04) ◽  
pp. 1350014
Author(s):  
MARK LIEBERMAN ◽  
RADU MURESAN ◽  
SIMON X. YANG ◽  
DAVID DOYLE
Keyword(s):  
Feature Extraction ◽  
Gas Sensors ◽  
Temperature Modulation ◽  
Hilbert Spectrum ◽  
Extraction Mechanism ◽  
Electronic Noses ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Potential Use

This work investigates the potential use of temperature modulation of MOS gas sensors combined with the Hilbert–Huang transform (HHT) as a feature extraction mechanism for MOS-based electronic noses. Five samples each of ethyl acetate, ethanol and isopropanol were prepared. The response of each of four sensors in an array was decomposed using empirical mode decomposition and the marginal Hilbert spectrum was computed. A set of 72 frequency components was extracted from marginal Hilbert spectrum response of each sensor in an array of four sensor to produce a 288 element fingerprint of each sample. The fingerprints were successfully clustered using PCA and classified using a SVM neutral network.

Leak Detection in Gas Pipeline Using Hilbert-Huang Transform

2015 ◽  
Vol 815 ◽  
pp. 403-407 ◽  
Author(s):  
Nurul Fatiehah Adnan ◽  
Mohd Fairusham Ghazali ◽  
Makeen M. Amin ◽  
A.M.A. Hamat
Keyword(s):  
Detection Method ◽  
Leak Detection ◽  
Pipeline System ◽  
Simple Method ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Leak Location ◽  
Mode Decomposition ◽  
Useful Signal

This paper proposes a leak detection method using acoustic. The Hamming chirp signal injected into the pipeline system and the estimation of the leak location from the delay time passing by the reflection in the pipeline if there is a leak. By using Hilbert-Huang Transform (HHT), it can give a useful signal to verify the leak. HHT transforms Empirical Mode Decomposition (EMD) and Hilbert Spectrum analysis to perform time-frequency analysis. The leak location can be detected by multiplying by the speed of sound. This simple method gives accurate leak location and easy to implement.

The Hilbert-Huang Spectral Analysis Method Applied to VIV

2006 ◽  
Author(s):  
Celso P. Pesce ◽  
Andre´ L. C. Fujarra ◽  
Leonardo K. Kubota
Keyword(s):  
Spectral Analysis ◽  
Stationary Time Series ◽  
Analysis Method ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Modulation ◽  
Mode Decomposition ◽  
Highly Nonlinear ◽  
Stationary Signals ◽  
Spectral Analysis Method

Vortex-Induced Vibration (VIV) is a highly nonlinear dynamic phenomenon. Usual spectral analysis methods rely on the hypotheses of linear and stationary dynamics. A new method envisaged to treat nonlinear and non-stationary signals was presented by Huang et al. [1] : The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. This technique, called thereafter the Hilbert-Huang transform (or spectral analysis) method, is here applied to VIV phenomena, aiming at disclosing some hidden dynamic characteristics, such as the time-modulation and jumps of multi-branched response frequencies and their related energy spectra.

Feature Extraction of the Crack AE Signal Using HHT Transform

2013 ◽  
Vol 340 ◽  
pp. 441-444
Author(s):  
K.F. He ◽  
Z.J. Zhang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Crack Depth ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Ae Signal

The use of Hilbert-Huang transform (Hilbert-Huang transform, HHT) on crack AE signal study, through empirical mode decomposition (empirical mode decomposition, EMD) AE signal is decompose into a number of intrinsic mode functions (Intrinsic mode Function, IMF), Hilbert spectrum and Hilbert marginal spectrum are calculated. The results show that crack depth structure bearing of acoustic emission are detected accurately by the number of acoustic emission events, time and crack the degree from Hilbert spectrum and Hilbert marginal spectrum.

NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (01) ◽  
pp. 25-37 ◽  
Author(s):  
PO-HSIANG TSUI ◽  
CHIEN-CHENG CHANG ◽  
NORDEN E. HUANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
The Core ◽  
Mode Decomposition ◽  
Threshold Amplitude ◽  
The Mean ◽  
Mixing Problem ◽  
Emd Method

The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.

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