scholarly journals On the Use of High-resolution Time-frequency Distribution Based on a Polynomial Compact Support Kernel for Fault Detection in a Two-level Inverter

2020 ◽  
Vol 64 (4) ◽  
pp. 352-365
Author(s):  
Sara Seninete ◽  
Mansour Abed ◽  
Azeddine Bendiabdellah ◽  
Malika Mimi ◽  
Adel Belouchrani ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Circuit ◽  
Frequency Resolution ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Insulated Gate Bipolar Transistor ◽  
Concentration Measure ◽  
Standard Tool ◽  
Time Frequency Localization ◽  
Frequency Components

Quadratic Time-Frequency Distributions (TFDs) become a standard tool in many fields producing nonstationary signatures. However, these representations suffer from two drawbacks: First, bad time-frequency localization of the signal's autoterms due to the unavoidable crossterms generated by the bilinear form of these distributions. This results on bad estimation of the Instantaneous Frequency (IF) laws and decreases, in our case, the ability to precisely decide the existence of a motor fault. Secondly, the TFD's parameterization is not always straightforward. This paper deals with faults' detection in two-level inverter feeding induction motors, in particular open-circuit Insulated Gate Bipolar Transistor (IGBT) faults. For this purpose, we propose the use of a recent high-resolution TFD, referred as PCBD for Polynomial Cheriet-Belouchrani Distribution. The latter is adjusted using only a single integer that is automatically optimized using the Stankovic concentration measure, otherwise, no external windows are needed to perform the highest time-frequency resolution. The performance of the PCBD is compared to the best-known quadratic representations using a test bench. Experimental results show that the frequency components characterizing open-circuit faults are best detected using the PCBD thanks to its ability to suppress interferences while maintaining the signal's proper terms.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

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.

Research on Coupling Faults With Oil-Film Instability and Bearing Rub-Impact Based on Reassigned Wavelet Scalogram

2007 ◽  
Author(s):  
Xiaopeng Li ◽  
Hui Ma ◽  
Guiqiu Song ◽  
Bangchun Wen
Keyword(s):  
Low Frequency ◽  
Decisive Role ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Oil Film ◽  
Rotor Systems ◽  
Time Frequency ◽  
Coupling Faults ◽  
Film Instability ◽  
Frequency Components

An experiment rig was set up to simulate coupling faults with oil-film instability and local rub-impact. The collected vibration signals were roughly analyzed by 3-D waterfall spectra. In complicated frequency components domain, the vibration signals were analyzed with wavelet scalogram and reassigned wavelet scalogram. Results show that reassigned wavelet scalogram has higher time-frequency resolution than wavelet scalogram and can well identify such close low-frequency components. The analysis results also indicated rub-impact faults induced by oil-film instability can produce some low-frequency components with small amplitude and oil whip can produce half frequency component; rub-impact with a common extent exerts a light influence on rotor systems and nonlinear oil-film force plays a decisive role in rotor systems.

A Method for Extracting Characteristic Frequency Components of Blood Flow Signals Based on Wavelet Transform

2013 ◽  
Vol 313-314 ◽  
pp. 1221-1224 ◽  
Author(s):  
Ruo Fei Cui ◽  
Si Te Luo ◽  
Li Qian Lu ◽  
Wei Wei Zhou ◽  
Zeng Yong Li
Keyword(s):  
Blood Flow ◽  
Wavelet Transform ◽  
Characteristic Frequency ◽  
Frequency Resolution ◽  
Peripheral Blood Flow ◽  
Frequency Interval ◽  
Frequency Method ◽  
Doppler Flowmetry ◽  
Time Frequency ◽  
Frequency Components

The objective of this paper is to propose a method for exacting the characteristic frequency components of blood flow signals based on wavelet transform. The wavelet transform technique, a time-frequency method with logarithmic frequency resolution, was used to analyze oscillations in human peripheral blood flow measured by laser Doppler flowmetry (LDF). In the frequency interval from 0.008 to 2.0 Hz, the LDF signal consists of components with five different characteristic frequenciesmetabolic (0.008-0.02Hz), neurogenic (0.02-0.06Hz), myogenic (0.06-0.15Hz), respiratory (0.15-0.4Hz) and cardiac (0.4-2.0Hz). The five frequency components were extracted in time domain and reconstructed using cubic spline interpolation in this study. The results showed that it was an effective way to extract each component of blood flow signals.

scholarly journals Time-Frequency Analysis of Clinical Percussion Signals Using Matrix Pencil Method

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Moinuddin Bhuiyan ◽  
Eugene V. Malyarenko ◽  
Mircea A. Pantea ◽  
Dante Capaldi ◽  
Alfred E. Baylor ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Matrix Pencil ◽  
Time Frequency Analysis ◽  
Frequency Distributions ◽  
Practical Applications ◽  
Time Frequency ◽  
Matrix Pencil Method ◽  
Oscillation Modes ◽  
Cross Terms ◽  
Time Frequency Localization

This paper discusses time-frequency analysis of clinical percussion signals produced by tapping over human chest or abdomen with a neurological hammer and recorded with an air microphone. The analysis of short, highly damped percussion signals using conventional time-frequency distributions (TFDs) meets certain difficulties, such as poor time-frequency localization, cross terms, and masking of the lower energy features by the higher energy ones. The above shortcomings lead to inaccurate and ambiguous representation of the signal behavior in the time-frequency plane. This work describes an attempt to construct a TF representation specifically tailored to clinical percussion signals to achieve better resolution of individual components corresponding to physical oscillation modes. Matrix Pencil Method (MPM) is used to decompose the signal into a set of exponentially damped sinusoids, which are then plotted in the time-frequency plane. Such representation provides better visualization of the signal structure than the commonly used frequency-amplitude plots and facilitates tracking subtle changes in the signal for diagnostic purposes. The performance of our approach has been verified on both ideal and real percussion signals. The MPM-based time-frequency analysis appears to be a better choice for clinical percussion signals than conventional TFDs, while its ability to visualize damping has immediate practical applications.

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