Fault Diagnosis of Rotating Machinery Due to Clearance Using Hilbert-Huang Transform

2009 ◽  
Author(s):  
Yeon-Sun Choi
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Frequency Information ◽  
Stationary Signals ◽  
Signal Processing Methods ◽  
The Impact ◽  
Conventional Signal

The faults in rotating machinery, caused by the clearance between the rotor and the stator, commonly lie on partial rub and looseness. These problems cause malfunctions in rotating machinery since they create strange vibrations coming from impact and friction. However, non-linear and non-stationary signals due to impact and friction are difficult to identify. Therefore, exact time and frequency information are needed for identifying these signals. For this purpose, a newly developed time-frequency analysis method, HHT(Hilbert-Huang Transform), is applied to the signals of partial rub and looseness from the experiment RK-4 rotor kit. Conventional signal processing methods such as FFT, STFT and CWT were compared to verify the effectiveness of fault diagnosis using HHT. The results showed that the impact signals were generated regularly when partial rub occurred but that intermittent impact and friction occurred irregularly when looseness occurred. The time and frequency information was represented exactly by using HHT in both cases, which makes clear fault diagnosis between partial rub and looseness.

An Improved HHT Method and its Application in Fault Diagnosis of Roller Bearing

2013 ◽  
Vol 273 ◽  
pp. 264-268 ◽  
Author(s):  
Ling Li Jiang ◽  
Bo Bo Li ◽  
Xue Jun Li
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Low Frequency ◽  
Analysis Method ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Effective Time ◽  
Coefficient Method

Hilbert-Huang transform (HHT) is a very effective time-frequency analysis method, but it has some disadvantages. For example, the dense modal signal cannot be decomposed completely, and redundancy intrinsic mode functions (IMFs) are easy emerging at low frequency, which will cause the distortion of the processing results. In view of the above questions, this study applies the wavelet packet transform for denoising before HHT for improving the dense modal problem, and applies the correlation coefficient method to eliminate the redundancy IMF. The fault diagnostic case of roller bearing shows the effectiveness of the proposed method.

Research on the Time-Frequency Analysis Method to Extract Early Fault Features of Rotating Machinery

2012 ◽  
Vol 190-191 ◽  
pp. 1371-1375
Author(s):  
Ping Hua Ju ◽  
Gen Bao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Mechanical Equipment ◽  
Analysis Method ◽  
Time Frequency ◽  
Fault Features ◽  
Common Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault ◽  
Basic Features

Early fault features of rotating machinery is very weak and is disturbed by strong noise generally. how to more accurately extract early (weak) fault features from signals is still a hot and difficult point of research of the discipline. An intensive study is given to basic features of rotating machinery early faults and common diagnosis method, And also summarized the research status of early diagnosis in the field of mechanical equipment signal feature extraction and fault diagnosis, analyzed the current problems, and finally briefly pointed out the development of early fault diagnosis in machinery applications.

METHOD FOR ENGINE WAVEFORM ANALYSIS AND FAULT DIAGNOSIS BASED ON SFB AND HHT

2013 ◽  
Vol 05 (04) ◽  
pp. 1350018
Author(s):  
SHUXIA JIANG ◽  
YIPING LUO ◽  
YUANYUAN LIU
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Pulse Width ◽  
Waveform Analysis ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
A New Technique ◽  
Injector Pulse ◽  
Mode Mixing

Traditional engine waveform analysis in time-domain fails to perform an accurate fault diagnosis when the fault waveform is very close to the normal waveform in time domain. A novel engine waveform analysis method is presented. In this paper, the aim is to perform fault diagnosis efficiently under such circumstances. This method is proposed by combining a new technique, called sensitive frequency band (SFB) selection, with the developed Hilbert–Huang transform (HHT). This can alleviate "mode mixing" by removing noise from the engine waveforms and reveal the time–frequency characteristics for a signal by deriving its time–frequency spectrum (TFS) distribution. The method is then applied to analyze the engine injector-pulse-width waveforms, and it works well for signal noise reduction and fault diagnosis.

Identification of Fault Signals in Rotating Machinery Using Higher Order Time Frequency Analysis

1997 ◽  
Author(s):  
Sang-Kwon Lee ◽  
Paul R. White
Keyword(s):  
Fault Diagnosis ◽  
Wigner Distribution ◽  
Higher Order ◽  
Rotating Machinery ◽  
Data Sets ◽  
Higher Order Statistics ◽  
Vibration Signals ◽  
Time Frequency ◽  
Increasing Trend ◽  
Monitoring Application

Abstract Impulsive acoustic and vibration signals within rotating machinery are often induced by irregular impacting. Thus the detection of these impulses can be useful for fault diagnosis. Recently there is an increasing trend towards the use of higher order statistics for fault detection within mechanical systems based on the observation that impulsive signals tend to increase the kurtosis values. We show that the fourth order Wigner Moment Spectrum, called the Wigner Trispectrum, has superior detection performance to second order Wigner distribution for typical impulsive signals found in a condition monitoring application. These methods are also applied to data sets measured within a car engine and industrial gearbox.

scholarly journals A Time–Frequency Correlation Analysis Method of Time Series Decomposition Derived from Synchrosqueezed S Transform

2019 ◽  
Vol 9 (4) ◽  
pp. 777 ◽  
Author(s):  
Gaoyuan Pan ◽  
Shunming Li ◽  
Yanqi Zhu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Time Varying ◽  
Analysis Method ◽  
Frequency Correlation ◽  
Time Frequency ◽  
Time Series Decomposition ◽  
S Transform ◽  
Stationary Signals ◽  
Correlation Analysis Method

Traditional correlation analysis is analyzed separately in the time domain or the frequency domain, which cannot reflect the time-varying and frequency-varying characteristics of non-stationary signals. Therefore, a time–frequency (TF) correlation analysis method of time series decomposition (TD) derived from synchrosqueezed S transform (SSST) is proposed in this paper. First, the two-dimensional time–frequency matrices of the signals is obtained by synchrosqueezed S transform. Second, time series decomposition is used to transform the matrices into the two-dimensional time–time matrices. Third, a correlation analysis of the local time characteristics is carried out, thus attaining the time–frequency correlation between the signals. Finally, the proposed method is validated by stationary and non-stationary signals simulation and is compared with the traditional correlation analysis method. The simulation results show that the traditional method can obtain the overall correlation between the signals but cannot reflect the local time and frequency correlations. In particular, the correlations of non-stationary signals cannot be accurately identified. The proposed method not only obtains the overall correlations between the signals, but can also accurately identifies the correlations between non-stationary signals, thus showing the time-varying and frequency-varying correlation characteristics. The proposed method is applied to the acoustic signal processing of an engine–gearbox test bench. The results show that the proposed method can effectively identify the time–frequency correlation between the signals.

scholarly journals Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

2020 ◽  
Vol 10 (18) ◽  
pp. 6376 ◽  
Author(s):  
Yihan Wang ◽  
Zhonghui Fan ◽  
Hongmei Liu ◽  
Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Entropy Method ◽  
Characteristic Matrix ◽  
Intrinsic Mode Functions ◽  
Noise Component ◽  
Time Frequency ◽  
Frequency Information ◽  
Operation Conditions ◽  
Mode Decomposition

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

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