Copula-Based Time-Frequency Distribution Analysis for Planetary Gearbox Fault Detection

2017 ◽  
Author(s):  
Libin Liu ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Energy Distribution ◽  
Simulated Data ◽  
Frequency Resolution ◽  
Data Sets ◽  
Distribution Analysis ◽  
Frequency Distributions ◽  
Copula Theory ◽  
Planetary Gearbox ◽  
Time Frequency

Linear and bilinear time-frequency distributions (TFDs) have been employed in planetary gearbox fault diagnosis. For linear TFDs, there is a trade-off between the time localization and frequency resolution and the spectrogram may not have correct energy marginals. For bilinear TFDs, they cannot be interpreted as an energy distribution because of the existence of possible negative values even though they are designed for energy density representation. To overcome these shortcomings, TFDs based on copula theory have been reported in the literature. In this paper, we analyze two simulated data sets using linear TFD and copula-based TFD. The results show that the constructed copula-based TFD has desirable properties of being positive, free from cross-term interference, having high time-frequency resolution and matching well with true marginals. The copula-based TFD is also able to locate fault-induced impulses by vertical lines over a certain frequency range in the time-frequency domain. Consequently, this study confirms the advantages of the copula-based TFD as an energy distribution and its capability in fault detection for planetary gearboxes.

An Evaluation of Linear Time Frequency Distribution Analysis for VSI Switch Faults Identification

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
M.F. Habban ◽  
M. Manap ◽  
A.R. Abdullah ◽  
M.H. Jopri ◽  
T. Sutikno
Keyword(s):  
Fault Detection ◽  
Frequency Distribution ◽  
Linear Time ◽  
Harmonic Distortion ◽  
Voltage Source ◽  
Distribution Analysis ◽  
Voltage Source Inverter ◽  
Time Frequency ◽  
S Transform ◽  
Time Frequency Distribution

This paper present an evaluation of linear time frequency distribution analysis for voltage source inverter system (VSI). Power electronic now are highly demand in industrial such as manufacturing, industrial process and semiconductor because of the reliability and sustainability. However, the phenomenon that happened in switch fault has become a critical issue in the development of advanced. This causes problems that occur study on fault switch at voltage source inverter (VSI) must be identified more closely so that problems like this can be prevented. The TFD which is STFT  and S-transform method are analyzed the switch fault of VSI.  To identify the VSI switches fault, the parameter of fault signal such as instantaneous of average current, RMS current, RMS fundamental current, total waveform distortion, total harmonic distortion and total non-harmonic distortion can be estimated from TFD. The analysis information are useful especially for industrial application in the process for identify the switch fault detection. Then the accuracy of both method, which mean STFT and S-transform are identified by the lowest value of mean absolute percentage error (MAPE). In addition, the S-transform gives a better accuracy compare with STFT and it can be implement for fault detection system.

scholarly journals S-Transform Based Analysis for Stock Market Volatility Estimation

2019 ◽  
Vol 8 (11) ◽  
pp. 3669-3675
Keyword(s):  
Financial Time Series ◽  
Theory And Practice ◽  
Frequency Resolution ◽  
Gaussian Kernel ◽  
Bench Mark ◽  
Data Sets ◽  
Stock Market Volatility ◽  
Asset Valuation ◽  
Time Frequency ◽  
S Transform

Financial Time series analysis (FTSA) is concerned with theory and practice of asset valuation over time. Generally, FTSA is useful for forecasting the asset volatility. This paper proposes the discrete S-Transform technique driven by Gaussian kernel for the estimation of volatility in FTSA. S-Transform is found to be a better tool in finding the time frequency resolution so as to predict and estimate the risk and returns of financial market. S-Transform prediction on two different bench mark data sets namely, Standard & Poor(S&P) 500 and Dow Jones Industrial Average(DJIA) index clearly indicates its superiority for the prediction of short and long-term trends in stock markets

Information Theoretic Equivalent Bandwidths of Random Processes and Their Applications

2007 ◽  
Vol 46 (02) ◽  
pp. 110-116 ◽  
Author(s):  
S. Kikkawa ◽  
H. Yoshida
Keyword(s):  
Random Process ◽  
Random Processes ◽  
Systolic Murmur ◽  
Nonstationary Random Process ◽  
Frequency Distributions ◽  
Copula Theory ◽  
Information Theoretic ◽  
Time Frequency ◽  
New Class ◽  
Stationary Random Processes

Summary Objectives : Since most of the biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and phonocardiogram (PCG), are nonstationary random processes, the time-frequency analysis has recently been extensively applied to those signals in order to achieve precise characterization and classification. In this paper, we have first defined a new class of information theoretic equivalent bandwidths (EBWs) of stationary random processes, then instantaneous EBWs (IEBWs) using nonnegative time-frequenc distributions have been defined in order to track the change of the EBW of a nonstationary random process. Methods : The new class of EBWs which includes spectral flatness measure (SFM) for stationary random processes is defined by using generalized Burg entropy. Generalized Burg entropy is derived from the relation between Rényi entropy and Rényi information divergence of order α. In order to track the change of EBWs of a nonstationary random process, the IEBWs are defined on the nonnegative time-frequency distributions, which are constructed by the Copula theory. Results : We evaluate the IEBWs for a first order stationary auto-regressive (AR) process and three types of time-varying AR processes. The results show that the IEBWs proposed here properly represent a signal bandwidth. In practical application to PCGs, the proposed method was successful in extracting the information that the bandwidth of the innocent systolic murmur was much smaller than that of the abnormal systolic murmur. Conclusions : We have defined new information theoretic EBWs and have proposed a novel method to track the change of the IEBWs. Some computer simulation showed effectiveness of the methods. Applying the IEBWs to PCGs, we could extract some features of a systolic murmur.

scholarly journals Time-Frequency Analysis Based on Minimum-Norm Spectral Estimation to Detect Induction Motor Faults

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4102
Author(s):  
Tomas A. Garcia-Calva ◽  
Daniel Morinigo-Sotelo ◽  
Oscar Duque-Perez ◽  
Arturo Garcia-Perez ◽  
Rene de J. Romero-Troncoso
Keyword(s):  
Fault Detection ◽  
Frequency Analysis ◽  
Induction Motors ◽  
Spectral Estimation ◽  
Diagnostic Techniques ◽  
Frequency Resolution ◽  
Minimum Norm ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Stationary Conditions

In this work, a new time-frequency tool based on minimum-norm spectral estimation is introduced for multiple fault detection in induction motors. Several diagnostic techniques are available to identify certain faults in induction machines; however, they generally give acceptable results only for machines operating under stationary conditions. Induction motors rarely operate under stationary conditions as they are constantly affected by load oscillations, speed waves, unbalanced voltages, and other external conditions. To overcome this issue, different time-frequency analysis techniques have been proposed for fault detection in induction motors under non-stationary regimes. However, most of them have low-resolution, low-accuracy or both. The proposed method employs the minimum-norm spectral estimation to provide high frequency resolution and accuracy in the time-frequency domain. This technique exploits the advantages of non-stationary conditions, where mechanical and electrical stresses in the machine are higher than in stationary conditions, improving the detectability of fault components. Numerical simulation and experimental results are provided to validate the effectiveness of the method in starting current analysis of induction motors.

Visualization of EEG Using Time-Frequency Distributions

1997 ◽  
Vol 36 (04/05) ◽  
pp. 298-301 ◽  
Author(s):  
B. Stiber ◽  
S. Sato
Keyword(s):  
Brain Activity ◽  
Temporal Structure ◽  
Frequency Resolution ◽  
Signal Frequency ◽  
Time Varying ◽  
Eeg Analysis ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Temporal Features ◽  
Time Varying Frequency

Abstract:The EEG is a time-varying or nonstationary signal. Frequency and amplitude are two of its significant characteristics, and are valuable clues to different states of brain activity. Detection of these temporal features is important in understanding EEGs. Commonly, spectrograms and AR models are used for EEG analysis. However, their accuracy is limited by their inherent assumption of stationarity and their trade-off between time and frequency resolution. We investigate EEG signal processing using existing compound kernel time-frequency distributions (TFDs). By providing a joint distribution of signal intensity at any frequency along time, TFDs preserve details of the temporal structure of the EEG waveform, and can extract its time-varying frequency and amplitude features. We expect that this will have significant implications for EEG analysis and medical diagnosis.

scholarly journals A Critical Review of Time-frequency Distribution Analysis for Detection and Classification of Harmonic Signal in Power Distribution System

2018 ◽  
Vol 8 (6) ◽  
pp. 4603
Author(s):  
M. H. Jopri ◽  
A. R. Abdullah ◽  
T. Sutikno ◽  
M. Manap ◽  
M. R. Ab Ghani ◽  
...  
Keyword(s):  
Critical Review ◽  
Power Distribution ◽  
Distribution System ◽  
Harmonic Signal ◽  
Distribution Analysis ◽  
Frequency Distributions ◽  
Power Distribution System ◽  
Time Frequency ◽  
Harmonic Signals

<p>This paper presents a critical review of time-frequency distributions (TFDs) analysis for detection and classification of harmonic signal. 100 unique harmonic signals comprise of numerous characteristic are detected and classified by using spectrogram, Gabor transform and S-transform. The rulebased classifier and the threshold settings of the analysis are according to the IEEE Standard 1159 2009. The best TFD for harmonic signals detection and classification is selected through performance analysis with regards to the accuracy, computational complexity and memory size that been used during the analysis.</p>

scholarly journals SPEAKER LOCALIZATION AND SPEECH SEPARATIONIN TWO ECHOIC MIXTURES / KALBĖTOJO APTIKIMAS IR ŠNEKOS IŠSKYRIMAS DVIEJŲ SIGNALŲ MIŠINIUOSE SU AIDU

2011 ◽  
Vol 3 (1) ◽  
pp. 43-49
Author(s):  
Włodzimierz Kasprzak ◽  
Ning Ding ◽  
Nozomu Hamada
Keyword(s):  
Energy Distribution ◽  
Estimation Error ◽  
Real Life ◽  
Low Frequency ◽  
Delay Estimation ◽  
Distribution Analysis ◽  
Time Frequency ◽  
Time Points ◽  
Fundamental Frequencies ◽  
Orientation Histogram

We are developing two crucial improvements on the time-frequency masking approach to the blind speech separation of underdetermined mixtures when processing anechoic and echoic mixtures. First, the proposed method copes with the usually large amount of delay estimation error that appears in a low frequency band. This step generates a restrictive mask for phase delays on the basis of local and global energy distribution analysis. This mask allows the selected cells to contribute to the orientation histogram. Second, the strong WDO assumption (disjoint orthogonal frequency domain) is relaxed by allowing some frequency bins to be shared by both sources. By detecting fundamental frequencies of speakers at instantaneous time points, mask creation is supported by exploring their harmonic frequencies. The proposed method is proved to be effective and reliable in conducting experiments with both simulated and real-life mixtures.

scholarly journals Application of Reassigned Wavelet Scalogram in Wind Turbine Planetary Gearbox Fault Diagnosis under Nonstationary Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowang Chen ◽  
Zhipeng Feng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Cross Term ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Gear Fault ◽  
Nonstationary Conditions

Wind turbine planetary gearboxes often run under nonstationary conditions due to volatile wind conditions, thus resulting in nonstationary vibration signals. Time-frequency analysis gives insight into the structure of an arbitrary nonstationary signal in joint time-frequency domain, but conventional time-frequency representations suffer from either time-frequency smearing or cross-term interferences. Reassigned wavelet scalogram has merits of fine time-frequency resolution and cross-term free nature but has very limited applications in machinery fault diagnosis. In this paper, we use reassigned wavelet scalogram to extract fault feature from wind turbine planetary gearbox vibration signals. Both experimental and in situ vibration signals are used to evaluate the effectiveness of reassigned wavelet scalogram in fault diagnosis of wind turbine planetary gearbox. For experimental evaluation, the gear characteristic instantaneous frequency curves on time-frequency plane are clearly pinpointed in both local and distributed sun gear fault cases. For in situ evaluation, the periodical impulses due to planet gear fault are also clearly identified. The results verify the feasibility and effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions.

scholarly journals Matching pursuit based on the rotated time-frequency atomic dictionary and its applications to seismic non-stationary data

2019 ◽  
Vol 16 (5) ◽  
pp. 822-841
Author(s):  
Pengfei Qi ◽  
Yanchun Wang
Keyword(s):  
Seismic Waves ◽  
Matching Pursuit ◽  
Frequency Characteristics ◽  
Thin Layers ◽  
Frequency Resolution ◽  
Data Sets ◽  
Time Frequency ◽  
Frequency Representation ◽  
Intrinsic Quality ◽  
Mp Method

Abstract The time-frequency spectrum of an attenuated seismic wave rotates due to the influence of intrinsic quality factor. Traditional time-frequency analysis methods are either limited by time-frequency resolution or incapable of rotated time-frequency representation. To solve it, a matching pursuit (MP) method based on the rotated time-frequency atomic dictionary (MPFR) was proposed. By introducing a frequency rotation factor to the four-parameter dictionary in the conventional MP method, a five-parameter dictionary with frequency rotation characteristics was constructed, and the calculation process of each parameter was given. We tested the method on the synthetic traces and field data sets. The results showed that the proposed method can effectively realize rotated time-frequency characterization of attenuated seismic waves. Compared with the conventional atoms, the rotated time-frequency atoms are more compliant to the local features of non-stationary seismic data. Moreover, the effective description of rotated time-frequency characteristics can be used as an auxiliary technique to predict the reservoirs related to the attenuation. A care has to be taken when thin layers are encountered, since wavelet interference may cause rotated time-frequency characteristics independent of dispersion and attenuation in MPFR method.

Investigation Frequency Resolution Effect on Hilbert Spectrum for Instantaneous Vibration Impact Signal Analysis

2009 ◽  
Vol 413-414 ◽  
pp. 135-142
Author(s):  
Hong Kun Li ◽  
Zhi Xin Zhang
Keyword(s):  
Pattern Recognition ◽  
Signal Analysis ◽  
Performance Estimation ◽  
Frequency Resolution ◽  
Distribution Analysis ◽  
Estimation Model ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Signal Features ◽  
Vibration Impact

Instantaneous impact signal analysis investigation and feature extraction have been broadly investigated by many researchers. How to determine an effective time-frequency distribution analysis method is urgent needed. In this paper, Hilbert spectrum frequency resolution is investigated to show signal typical features according to performance estimation model. Different frequency units for a Hilbert spectrum expression will be investigated on the effect of feature analysis in detail. An estimation model will be constructed to determine the best frequency resolution for signal analysis. By using this estimation, frequency resolution can be determined. This model can improve the performance of Hilbert spectrum to show signal features and accuracy for signal analysis and pattern recognition. To testify the effectiveness of this estimation model, numerical simulation will be used as an example to analyze the accuracy of this model. At the same time, different bearing working condition’s pattern recognition based on Hilbert spectrum will be used to testify the effectiveness of this model.

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