This paper presents a novel ultrasonic guided wave based inspection methodology for detecting and evaluating gas
accumulation in nuclear cooling pipe system. The sensing is in-situ by means of low-profile permanently installed
piezoelectric wafer sensors to excite interrogating guided waves and to receive the propagating waves in the pipe
structure. Detection and evaluation is established through advanced cross time-frequency analysis to extract the phase
change in the sensed signal when the gas is accumulating. A correlation between the phase change and the gas amount
has been established to provide regulatory prediction capability based on measured sensory data.
KEYWORDS: Time-frequency analysis, Diagnostics, Algorithm development, Signal analyzers, Signal detection, Information theory, Data acquisition, Sensors, Electronic filtering, Thin film coatings
The classical time-frequency distributions represent time- and frequency-localized energy. However, it is not an
easy task to analyze multiple signals that have been simultaneously collected. In this paper, a new concept of
non-parametric detection and classification of the signals is proposed using the mutual information measures in the
time-frequency domain. The time-frequency-based self and mutual information is defined in terms of cross time-frequency
distribution. Based on the time-frequency mutual information theory, this paper presents applications
of the proposed technique to real-world vibration data. The baseline and misaligned experimental settings are
quantitatively distinguished by the proposed technique.
KEYWORDS: Time-frequency analysis, Signal processing, Systems modeling, Digital signal processing, Electrical breakdown, Modeling and simulation, Phase shifts, Device simulation, Dielectrics, Intelligence systems
This paper draws on an innovative, signal processing-based method that jointly analyzes the time and frequency
domains and uses that information to characterize and distinguish the deadly arc faults from the normal operational
faults. This paper introduces a variety of new power quality assessment tools developed with the purpose of both
detecting an arc fault faster than has yet been done and distinguishing the arc fault from other normal load operations
via time-localized spectral characterization. Based on the time and frequency localization of the arc faults, the time
varying impedances of the arc fault are modeled in terms of harmonic sources. The accomplishment of these objectives
would lead to new, advanced smart arc fault circuit breakers and the modeling & simulation of arc fault phenomena.
For an assessment of the power quality in power distribution systems, classical Fourier series-based power quality indices are normally employed. The classical Fourier series-based power quality indices assume the periodicity of the disturbance so that the applications are limited to the harmonics. Hence, it is necessary for us to redefine power quality indices for the "transient" disturbances. In this paper, development of time-frequency based power quality indices are discussed for an assessment of transient power quality. The time and frequency localized information of the transient disturbance signals will be utilized for a new definition of the transient power quality indices. As an example of time-frequency based power quality indices, new definition of transient telephone interference factor has been carefully derived and verified in comparison with traditional telephone interference factor. Time-frequency based power quality indices allow one to quantify the effects of transient disturbances by time and frequency localized information.
In electric power systems, the flow of electric power is an
important issue for the control and management of the system.
However, under transient-states caused by electrical disturbances,
it is not a simple task to determine the flow of transient
disturbance energy in an analytic way with high accuracy. The
proposed algorithm for the determination of transient disturbance
energy flow is based on cross time-frequency analysis that
provides time- and frequency- localized phase difference
information. Hence, based on the cross time-frequency distribution
of the transient voltage and current, the classical parameters in
power systems are modified for transient analysis. The transient
power factor angle will determine the direction of transient
disturbance energy (real and reactive) flows in power distribution
system networks. For the verification of the proposed algorithm, a
practical model of a power system is simulated by EMTP
(Electromagnetic Transient Program). In addition, knowledge of
this nature should greatly facilitate automatic identification of
transient events and determination of the physical location of the
source of various transient disturbances.
A new impedance measurement methodology based on time-frequency
domain reflectometry (TFDR) is proposed. For the evaluation of the
reflection coefficient in time-frequency domain reflectometry, the
distortion of the reflected wave by the frequency-dependent
attenuation is compensated which otherwise results in inaccurate
impedance measurement. Also, the phase difference between the
incident and reflected waveforms caused by the state of the load
impedance is evaluated by the cross time-frequency distribution
which provides time-frequency localized phase difference
information. The proposed methodology is verified by a set of
numerical electromagnectic simulation experiments and the results
are compared with classical time domain reflectometry (TDR).
Impedance measurement via time-frequency domain reflectometry is
more accurate over a wider range of impedances than TDR.
In this paper, a new high resolution reflectometry scheme, time-frequency domain reflectometry, is proposed to detect and locate a fault in wiring. Traditional reflectometry methods have been achieved in either the time domain or frequency domain only. However, time-frequency domain reflectometry utilizes time and frequency information of a transient signal to detect and locate the fault. The time-frequency domain reflectometry approach described in this paper is characterized by time-frequency reference signal design and post-processing of the reference and reflected signals to detect and locate the fault. Using a computational electromagnetic model of a coaxial cable with a fault, time-frequency domain reflectometry has been demonstrated. Knowledge of time and frequency localized information for the reference and reflected signal gained via time-frequency analysis, allows one to detect the fault and
estimate the location accurately.
In this paper, we consider cross time-frequency distributions, with particular emphasis on their ability to preserve phase difference (between two signals) information as a function of time and frequency. In addition, some properties of cross time-frequency distributions are examined and compared with the traditional Cohen's class of time-frequency distributions. Finally, the phase preserving properties of appropriately defined cross time-frequency distributions are illustrated with a pair of Gabor logons.
Wavelet analysis of voltage sag completely depends on the choice of the wavelet basis. For better detection performance via wavelet analysis, the choice of the optimal wavelet basis must be provided within the constraints of the uncertainty principle which restricts arbitrary assignment of time-frequency resolution. In this paper, we describe local properties of the wavelet basis and voltage sag signal in terms of time duration and frequency bandwidth parameters. After comparison of the local properties of the wavelet basis and voltage sag signal, we suggest a set of performance indexes to measure the time-frequency resolution relation between the wavelet basis and the voltage sag signal. This procedure of determining the optimal wavelet basis can be extended to other possible applications of wavelets.
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