David Coats

Health Monitoring of Power Cable via Joint Time-Frequency Domain Reflectometry

Utilities are experiencing premature failures of power cables. In order to prevent electrical outages and to save on repair expenses, a nondestructive and nonintrusive condition assessment technique is highly desirable to evaluate the cable status and to predict the remaining life of a cable. In this paper, the capability of joint time-frequency domain reflectometry (JTFDR) as such a condition assessment technique is studied. The health status of three popular insulations in power system cables - cross-linked polyethylene, ethylene propylene rubber, and silicone rubber - is monitored using the JTFDR in a thermal accelerated aging test. The experimental results show that the JTFDR can successfully monitor the aging process of all three insulations. Then, the results from the JTFDR are compared with the results from the elongation at break (EAB); the results show that the JTFDR technique is comparable with the EAB and has a great potential as a nondestructive and nonintrusive condition assessment technique.

Advanced Time–Frequency Mutual Information Measures for Condition-Based Maintenance of Helicopter Drivetrains

A new concept of nonparametric signal detection and classification technique is proposed using mutual information measures in the time-frequency domain. The time-frequency-based self-information and mutual information are defined in terms of the cross time-frequency distribution. Based on time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data obtained from a dedicated condition-based-maintenance experimental test bed. Baseline, unbalanced, and misaligned experimental settings of helicopter drivetrain bearings and shafts are quantitatively distinguished by the proposed techniques. With imbalance quantifiable by variance in the in-phase mutual information and misalignment quantifiable by variance in the quadrature mutual information developed and presented herein, machine health classification can be accomplished by use of statistical bounding regions.

Sub-synchronous resonance mitigation in wind farms using gate-controlled series capacitor

The increasing deployment of wind power generation is leading to the integration of large wind farms into the power distribution grid. Given the remote geographic location of wind farms, series capacitive compensation is commonly used to ensure stable power transmission over long distances. However, the sub-synchronous resonance (SSR) phenomenon presents potential risks in a series compensated wind farm. Although the SSR problem in traditional power systems is well-known and has been extensively studied in the literature, the SSR problem in series-compensated wind farms requires more study and analysis. This paper investigates the application of the gate-controlled series capacitor (GCSC), a new series FACTS device consisting of a fixed capacitor in parallel with a pair of anti-parallel GTOs, for series compensation in fixed-speed wind turbine generator (FSWTG) systems. The GCSC enables fast control of series impedance of the transmission line, which can be used for SSR damping. The power system studied in this paper is the IEEE First Benchmark Model. Extensive simulations are carried out using PSCAD/EMTDC to validate the result. Simulation results show that the GCSC can effectively damp the SSR in wind farms. Therefore, the GCSC is an effective solution to provide series compensation and SSR damping for the FSWTG.

Analysis of nonlinear vibration-interaction using higher order spectra to diagnose aerospace system faults

For efficient maintenance of a diverse fleet of aging air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. Traditional linear spectral analysis techniques of the vibration signals, based on auto-power spectrum, are used as common tools of rotating components diagnoses. Unfortunately, linear spectral analysis techniques are of limited value when various spectral components interact with one another due to nonlinear or parametric process. In such a case, higher order spectral (HOS) techniques are recommended to accurately and completely characterize the vibration signals. Since the nonlinearities result in new spectral components being formed with coherency in phase, the detection of such phase coherence may be carried out with the aid of higher order spectra. In this paper, we use the bispectrum as a higher order spectral analysis tool to investigate nonlinear wave-wave interaction in vibration signals. Accelerometer data has been collected from baseline tests of accelerated conditioning in tail rotor drive-train components of an AH-64 helicopter drive-train research test bed simulating drive-train conditions. Through bispectrum analysis, we compare the harmonics interaction patterns contained in vibration signals from different physical setting of helicopter drive train and compare that with classical power spectral density plots. The analysis advances the development of higher order statistics and two dimensional frequency health indicators in order to qualify health conditions in mechanical systems.

Applications of time-frequency analysis for aging aircraft component diagnostics and prognostics

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.

Design of advanced time-frequency mutual information measures for aerospace diagnostics and prognostics

Accelerometer data has been gathered from accelerated conditioning in grease lubricated and lubrication deprived gear meshes in AH-64 helicopter intermediate and tail rotor gearbox, which are commonly problematic components of the Apache helicopter platform. These tests were performed in a controlled drive-train research test bed, simulating drive-train conditions to improve diagnostic and prognostic capabilities of Condition Based Maintenance (CBM) practices in Integrated Vehicle Health Monitoring System - Health Usage Monitoring System (IVHMS-HUMS) and other comparable CBM packages, as monitored by a standardized Digital Source Collector (DSC) system. Time-frequency representations of vibration measurement collected from two spaced sensors are used to provide signature analysis of transient system harmonics. Furthermore, the time-frequency mutual information advanced signal processing technique is then proposed and validated using vibration data. The measure advances the development of mutual information health indicators to quantify degradation of the helicopter power train. The AH-64 test systems perform under stress in realistic loading conditions and lifetime accelerated aircraft aging is monitored using the proposed advanced signal processing techniques for baseline tests for comparison with faulted conditions.1 2

Quadratic-nonlinearity power-index spectrum and its application in condition based maintenance (CBM) of helicopter drive trains

This paper introduces a quadratic-nonlinearity powers-index spectrum (QNLPI(f)) measure that describes quantitatively how much of the mean square power at certain frequency f is generated by nonlinear quadratic interaction between different frequencies inside signal spectrum. The proposed index QNLPI(f) is based on the bicoherence spectrum, and the index can be simply seen as summary of the information contained in the bicoherence spectrum in two dimensional graph which makes it easier to interpret. The proposed index is studied first using computer generated data and then applied to real-world vibration data from a helicopter drive train to characterize different mechanical faults. This work advances the development of health indicators based on higher order statistics to assess fault conditions in mechanical systems.

Joint time-frequency optimized reference for surface wave reflectometry-based insulation health assessment

In this paper, we present a time-frequency based reflectometry solution that focuses on the practical implementation of in-situ and non-destructive cable diagnostic tests. Towards this ideal health monitoring approach, we have explored the implementation of alternative monitoring techniques using surface wave injection for monitoring of cable insulation health toward nondestructive tests. Through use of surface wave propagation, a diagnostic signal can be injected in control, instrumentation, and power cable without removing installed samples under test. However, in practical implementation of surface wave tests through antenna, problems are presented in the form of dispersive media distorting the response of the frequency sweeping reference signal in either frequency or time-frequency domains. We present an optimal reference signal and time-frequency cross correlation diagnostic and prognostic algorithm to allow for implementation of joint time-frequency domain reflectometry (JTFDR) in minimal bandwidth to reduce attenuation in surface wave reflectometry.

A wavelet–based index for fault detection and its application in condition monitoring of helicopter drive–train components

This paper presents a new condition indicator using wavelet analysis for the purpose of fault detection in an AH–64 gearbox. Historically, vibration–based condition indicators from employed component monitoring equipment are derived from both temporal and spectral domain analysis. However, these indicators failed to accurately capture high order correlations for the gearbox study addressed in this paper. An improved approach is necessary to overcome limitations of traditional vibrational monitoring techniques. The proposed condition indicator is derived from the Morlet continuous wavelet .transform The power spectra obtained from the wavelet transform coefficients at a certain scale or frequency are added together and then are normalised to one composite signal, denoted by a numeric index. Concepts of the wavelet index are discussed. This index is applied using real–world vibration data from a tail rotor gearbox with an output seal leak as part of condition–based maintenance practices. Results demonstrate potential of the proposed wavelet index to more effectively capture the fault when compared to gearbox condition indicators. [Received 24 January 2014; Revised 28 August 2014; Accepted 3 October 2014]

A new method to estimate the average dielectric constants of aged power cables

A new method to estimate the average dielectric constants of cable sections that have undergone aging is proposed. By comparing the experimental Joint Time-Frequency Domain Reflectometry waveform signatures from a new and an aged cable of the same type, it is demonstrated that the change in the average dielectric constant of the insulation material due to aging can be estimated. For example, for a cable containing Cross-Linked Polyethylene insulation, accelerated aging tests based on the modified Arrhenius equation that simulate 120 years of aging at 50 °C operating temperature show that the dielectric constant of the insulation decreases by more than 34.5%. The same tests performed on another cable containing Ethylene-Propylene Rubber insulation show that the average dielectric constant of the insulation decreases by 10.4%. The efficacy of the method is further demonstrated by estimating the increase in the average dielectric constant of a wedge section of a cable that contained water intrusion.