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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.

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.

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.

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.