Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
T. Y. Wu ◽  
Y. L. Chung ◽  
C. H. Liu
Keyword(s):  
Significance Test ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Test Bed ◽  
Statistical Concept ◽  
Transform Method ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Fault Indicator

The objective of this research in this paper is to investigate the feasibility of utilizing the Hilbert–Huang transform method for diagnosing the looseness faults of rotating machinery. The complicated vibration signals of rotating machinery are decomposed into finite number of intrinsic mode functions (IMFs) by integrated ensemble empirical mode decomposition technique. Through the significance test, the information-contained IMFs are selected to form the neat time-frequency Hilbert spectra and the corresponding marginal Hilbert spectra. The looseness faults at different components of the rotating machinery can be diagnosed by measuring the similarities among the information-contained marginal Hilbert spectra. The fault indicator index is defined to measure the similarities among the information-contained marginal Hilbert spectra of vibration signals. By combining the statistical concept of Mahalanobis distance and cosine index, the fault indicator indices can render the similarities among the marginal Hilbert spectra to enhanced and distinguishable quantities. A test bed of rotor-bearing system is performed to illustrate the looseness faults at different mechanical components. The effectiveness of the proposed approach is evaluated by measuring the fault indicator indices among the marginal Hilbert spectra of different looseness types. The results show that the proposed diagnosis method is capable of classifying the distinction among the marginal Hilbert spectra distributions and thus identify the type of looseness fault at machinery.

Mixed-Mode Signal Detection of Road Vehicle Vibration Using Hilbert-Huang Transform

2015 ◽  
Author(s):  
Julien Lepine ◽  
Michael Sek ◽  
Vincent Rouillard
Keyword(s):  
Mixed Mode ◽  
Vibration Signal ◽  
Significance Test ◽  
Mathematical Framework ◽  
Vehicle Speed ◽  
Road Vehicle ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Vibration

The Hilbert-Huang Transform (HHT) is a fully adaptive time-frequency analysis method which is applicable to nonlinear and nonstationary processes. However, this promising method is fairly new and its range of applications is not well known. Furthermore, its mathematical framework is not yet fully developed. So far, the HHT has yielded interesting results for many applications such as biomedical, geophysical, meteorological and health monitoring, but there is no evidence of its application on complex mixed-mode vibration signals. To fill that gap, this paper investigates the application of the HHT to detect the different modes of road vehicle vibration signals. These modes originate from road roughness variation and vehicle speed which create nonstationary random vibration. Other modes are due to road surface aberrations which create transient events and the engine and drive train system of the vehicle which create harmonic vibrations. The energy density/average period significance test based on the HHT is assessed to detect these modes. The results, based on purposefully created synthetic test signals, reveal the limitations and shortcomings of the HHT based technique to detect and separate the various components of the mixed-mode vibration signals such as vehicle vibration signal.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Characterization of Cavitation During Pump Operation Based on Hilbert-Huang Transform

2018 ◽  
Author(s):  
Hui Sun ◽  
Shouqi Yuan ◽  
Yin Luo ◽  
Bo Gong
Keyword(s):  
Centrifugal Pump ◽  
Longitudinal Vibration ◽  
Process Time ◽  
Current Signal ◽  
Vibration Signals ◽  
Pump Operation ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Cavitation Process ◽  
Non Stationary Signal

Cavitation has negative influence on pump operation. In order to detect incipient cavitation effectively, experimental investigation was conducted to through acquisition of current and vibration signals during cavitation process. In this research, a centrifugal pump was modeled for research. The data was analyzed by HHT method. The results show that Torque oscillation resulted from unsteady flow during cavitation process could result in energy variation. Variation regulation of RMS of IMF in current signal is similar to that in axial vibration signal. But RMS of IMF in current signal is more sensitive to cavitation generation. It could be regarded as the indicator of incipient cavitation. RMS variation of IMF in base, radial, longitudinal vibration signals experiences an obvious increasing when cavitation gets severe. Such single variation regulation could be selected as the indicator of cavitation stage recognition. Hilbert-Huang transform is suitable for transient and non-stationary signal process. Time-frequency characteristics could be extracted from results of HHT process to reveal pump operation condition. The contents of current work could provide valuable references for further research on centrifugal pump operation detection.

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.

Research of Aircraft Cable Fault Detecting Method Based on Hilbert Huang Transformation

2011 ◽  
Vol 214 ◽  
pp. 138-143
Author(s):  
Tao Jing ◽  
Lu Zhang ◽  
Xu Dong Shi ◽  
Li Wen Wang
Keyword(s):  
Frequency Domain ◽  
Short Circuit ◽  
Reference Signal ◽  
Open Circuit ◽  
Time Domain Reflectometry ◽  
Transform Method ◽  
Hilbert Huang Transform ◽  
Intermittent Faults ◽  
Time Frequency ◽  
Frequency Domain Reflectometry

Aircraft cable fault diagnosing is considered to be most important for engineering maintenance. Several methods for cables testing have been developed, such as TDR, FDR and TFDR. Time Domain Reflectometry (TDR) relays much on impedance changes on the fault position, which is hard to using in detecting high resistance defects, intermittent defects; Time Frequency Domain Reflectometry (TFDR) method is used to locate intermittent faults, continuous faults and cross-connection faults aircraft wire, however, the algorithm of TFDR is complex. To the "Hard Fault"(short circuit and open circuit), the Hilbert-Huang Transform method is used in determining the optimal bandwidth of the incident reference signal and analyzing the phase and amplitude difference of superimposed signal which from the incident signal and the reflected signal on defects. To the "Fray Fault", Time and Frequency Domain Reflectometry method can be used with the signal processing method with Hilbert-Huang Transform. The experimental results indicate that this method effectively detect all types of aircraft cable fault, particularly for short lengths of cable.

Stationarity test of vibration signals with surrogate data and time–frequency analysis

2016 ◽  
Vol 20 (8) ◽  
pp. 1143-1154
Author(s):  
Zuo-Cai Wang ◽  
Feng Wu ◽  
Wei-Xin Ren
Keyword(s):  
Frequency Analysis ◽  
Surrogate Data ◽  
Transform Method ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Stationarity Test ◽  
Modulated Signals ◽  
Short Time

The stationarity test of vibration signals is critical for the extraction of the signal features. In this article, the surrogate data with various time–frequency analysis methods are proposed for stationary test of vibration signals. The surrogate data are first generated from the Fourier spectrum of the original signal with keeping the magnitude of the spectrum unchanged and replacing its phase by a random sequence. The local and global spectra of the original signal and the surrogate data are then estimated by four time–frequency analysis methods, which are short-time Fourier transform, multitaper spectrograms, wavelet transform, and S-transform methods. The index of nonstationarity is then defined based on the distances between the local and global spectra. Three kinds of synthetic signals, which are stationary signals, frequency-modulated signals, and amplitude-modulated signals, are tested to compare the efficiency of the four time–frequency analysis methods as mentioned. The results show that with a certain observation scale value, the index of nonstationarity based on the short-time Fourier transform or wavelet transform method may fail to test the stationarity of the signal. The parametric studies and sensitivity analysis of the observation scale and noise-level effect are also extensively conducted. The results show that the index of nonstationarity calculated using the multitaper spectrograms’ method is more suitable for stationarity test of frequency-modulated signals, while the index of nonstationarity calculated using the S-transform method is more suitable for stationarity test of amplitude-modulated signals. The results also show that the noise has a significant effect on the stationarity test results. Finally, the stationarity of a real vibration signal measured from a cable is tested, and the results show that the proposed index of nonstationarity can effectively test the stationarity of real vibration signals.

Overview of Time-Frequency Analysis Techniques in Vibration Signals of Rotating Machinery

2014 ◽  
Vol 684 ◽  
pp. 124-130
Author(s):  
Hong Li ◽  
Qing He ◽  
Zhao Zhang
Keyword(s):  
Frequency Analysis ◽  
Local Characteristic ◽  
Vibration Signals ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Advantages And Disadvantages ◽  
Analysis Methods ◽  
Short Time

There is very rich fault information in vibration signals of rotating machineries. The real vibration signals are nonlinear, non-stationary and time-varying signals mixed with many other factors. It is very useful for fault diagnosis to extract fault features by using time-frequency analysis techniques. Recent researches of time-frequency analysis methods including Short Time Fourier Transform, Wavelet Transform, Wigner-Ville Distribution, Hilbert-Huang Transform, Local Mean Decomposition, and Local Characteristic-scale Decomposition are introduced. The theories, properties, physical significance and applications, advantages and disadvantages of these methods are analyzed and compared. It is pointed that algorithms improvement and combined applications of time-frequency analysis methods should be researched in the future.

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 Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition

2019 ◽  
Vol 9 (10) ◽  
pp. 2017 ◽  
Author(s):  
Juncai Xu ◽  
Bangjun Lei
Keyword(s):  
Instantaneous Frequency ◽  
Data Interpretation ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Synthetic Signal ◽  
Mode Decomposition ◽  
The Media ◽  
Ground Penetrating

Data interpretation is the crucial scientific component that influences the inspection accuracy of ground penetrating radar (GPR). Developing algorithms for interpreting GPR data is a research focus of increasing interest. The problem of algorithms for interpreting GPR data is unresolved. To this end, this study proposes a sophisticated algorithm for interpreting GPR data with the aim of improving the inspection resolution. The algorithm is formulated by integrating variational mode decomposition (VMD) and Hilbert–Huang transform techniques. With this method, the intrinsic mode function of the GPR data is first produced using the VMD of the data, followed by obtaining the instantaneous frequency by using the Hilbert–Huang transform to analyze the intrinsic mode functions. The instantaneous frequency data can be decomposed into three frequency attributes, including frequency division section, time-frequency section, and space frequency section, which constitute a platform to gain insight into the nature of the GPR data, such that the inspected media components can be examined. The effectiveness of the proposed method on a synthetic signal from a GPR forward model was studied, with the multi-resolution performance being tested. Inspecting the media of a highroad by analyzing the GPR data, with the abnormal characteristics being designated, validated the applicability of the proposed method.

Multi-Dimensional Directional Spectrograms and Campbell (ZMOD) Diagrams for Rotating Machinery

1995 ◽  
Author(s):  
I. Bucher ◽  
D. J. Ewins ◽  
D. A. Robb ◽  
P. Schmiechen
Keyword(s):  
Rotating Machinery ◽  
Experimental Results ◽  
New Method ◽  
Frequency Content ◽  
Rotating Disc ◽  
Vibration Signals ◽  
Time Frequency ◽  
Spatial Response ◽  
Spatial Domains ◽  
Vibration Pattern

Abstract A new method for decomposition of vibration signals measured on rotating machinery is presented in this paper. The method uses a signal measured from a number of sensors to decompose the spatial response according to the direction of the progression of motion, the frequency content and the various wavelengths. For the simple case of shaft vibration, two sensors, horizontal and vertical, are used to separate the vibration pattern into forward and backward progressing components. For the case of a rotating disc, more sensors are required to further decompose the response into different wavelengths. This allows one to monitor and to identify potentially dangerous vibration patterns exhibiting large backward components. The method is shown to provide better resolution in the time-frequency (speed of rotation) and spatial domains by separating several, usually overlapping patterns. Several analytical and experimental results demonstrate the usefulness of the proposed method.

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