Time-frequency Cross Correlation Research Articles

Fault detection and localization in unshielded twisted pair cable based on observability analysis and reflectometry experimental measurements

In this paper, an efficient method is proposed for detecting and locating a soft fault, i.e. a physical degradation, in a Y-shaped network of unshielded twisted pair cables. The method uses jointly the observability analysis and time-frequency domain reflectometry experimental measurements. The fault detection consists in checking the consistency between the known characteristics of the cable under no-fault and faulty conditions, with the maximum value of a normalized time-frequency cross-correlation function computed on a sliding time window whose length is equal to the boundary exact observability time. The cable transmission speed and the wired network observability time are combined to locate precisely the fault. Soft faults are artificially created by connecting resistors of different values to the test cable. Experimental results show the efficiency of our fault detection and localization method.

Application of Enhanced Optimal-Detection of Time–Frequency Domain Reflectometry on HTS Cable With High-Resolution

Time–frequency domain reflectometry (TFDR) has been used for sensing various types of cables including high temperature superconducting (HTS) cables. Recently, the optimal-detection of time–frequency domain reflectometry (OD-TFDR) has been proposed and has better detection and localization performance than conventional TFDR. Additionally, OD-TFDR has provided a threshold that can distinguish detected signals from Gaussian noise. However, OD-TFDR does not provide a criterion for classifying the detected signals into fault signals and artifacts. Furthermore, the diagnosis result of OD-TFDR is not intuitive because an additional tool is needed to locate each fault. Thus, this paper proposes a new diagnosis algorithm that can provides a criterion for distinguishing between artifacts and faults by considering the propagation direction of the incident signal in the join time–frequency domain while preserving the detection and localization performance of OD-TFDR. Through a template matching process of generalized time–frequency cross-correlation (GTFCC), the core algorithm of OD-TFDR, a modified–generalized time–frequency cross-correlation (M–GTFCC) function is derived. Next, a modified–time–frequency cross-correlation (M–TFCC) function is derived by slicing the M–GTFCC function considering the propagation direction of signal. Finally, M–TFCC function is proposed as a new intuitive diagnosis result with artifact-free and high-resolution. The performance of the proposed algorithm is tested via experimental setup for a real-world single phase HTS cable with an emulated local quench by heating element, and the efficacy of the proposed algorithm is verified based on three performances: fault detection, locating accuracy, and resolution.

Quench Localization for 2G High-Temperature Superconductor Tape Using Acoustic Reflectometry

Quench localization is one of the most important aspects in identifying the performance limitations of high-temperature superconductor (HTS) devices and applications. In order to localize the quench and improve spatial resolution, an acoustic-based quench detection technique using shear-horizontal waves and chirplet transform is proposed in this paper. To verify the performance of the proposed method, acoustic signals are collected by the shear piezoelectric transducers mounted on the REBCO tape, and the heating points are localized via the time-frequency cross-correlation value and baseline subtraction method. This work proves heating points can be detected and localized with a resolution of better than 1 %. It is expected that the proposed method can improve the quench detection and localization for accelerators and fusion power applications.

Dispersion analysis of multi-modal waves based on the Reassigned Cross-S-Transform

The quantitative description of the propagation of the energy components in a signal is a fundamental task in signal processing. The correct identification of the components is important because they carry information about the medium in which they propagate. In this article, I introduce the Reassigned Cross-S-Transform to estimate dispersion curves from time-varying signals, a technique based on function maximization principles and time-frequency cross-correlation. The RCST provides a sharpened and direct estimate of the dispersion curve for multi-modal propagating waves. Furthermore, the RCST can be used with signals that are not synchronized with the same initial time. I present examples studying the degradation effects on the slowness curves due to mode mixture and reflected waves. I illustrate the application of the RCST technique to artificially generated Ricker wavelets, and to real recordings of seismic waves generated by an active source. Finally, using 2D numerical simulations of wave propagating through an elastic layered medium, I solve a mode identification problem by combining the RCST with a previously proposed technique to extract Rayleigh waves from signals.

Channel modelling for vehicle‐to‐vehicle MIMO communications in geometrical rectangular tunnel scenarios

This study presents a multiple-input multiple-output (MIMO) statistical channel model for vehicle-to-vehicle (V2V) line-of-sight (LOS) and non-LOS mobile communication system in a rectangular tunnel environment. In order to improve the V2V communication system performance, the authors introduced the elevation and azimuth angles in three-dimensional modelling based on the original literature. In the proposed reference channel model, the single-bounced scattering propagation path of electromagnetic signals is considered and it is assumed that the scatterers are randomly distributed on the rectangular tunnel wall. Starting from the statistical physical channel model, the time-variant transfer functions are derived. Thereafter, the analytical expressions of the space–time–frequency cross-correlation function, the space cross-correlation function, the temporal autocorrelation function, the frequency correlation function, and MIMO channel capacity are derived. The influence of model parameters on the performance of the V2V communication system is also analysed. This work provides an innovative approach to channel modelling in a rectangular tunnel mobile communication environment.

Advanced Signal Sensing Method With Adaptive Threshold Curve Based on Time Frequency Domain Reflectometry Compensating the Signal Attenuation

Adaptive fault detection method, which is compensating the wave attenuation along the propagation, is proposed in this article. Long cable system installed in the factory need to develop precise fault detection method considering propagation dispersion. Using the derived time delay between the incident and reflected wave which is generated from the fault, fault can be localized. The time-frequency cross correlation (TFCC) is a signal scanning process based on the similarity of the energy spectrum for detecting the reflected signal mixed with the noise. The threshold value in TFCC is a reference constant value for judging whether or not a fault is present. Due to the attenuation and dispersion of signal along the propagation, a new threshold value function considering attenuation and dispersion is needed. We propose the adaptive threshold curve for compensating the signal attenuation. We design the equivalent circuit of target cable ( $\text{7}\,\text{m}$ length) and optimal reference signal based on the comparative analysis between the results of computer simulation and actual experiments of $\text{7}\,\text{m}$ length cable. Based on the designed equivalent circuit of target cable( $\text{7}\,\text{m}$ length), a long cable system is modeled by simulation and the performance of the proposed method is proved through the simulation under the various noise level environments.

Wire Mismatch Detection Using a Convolutional Neural Network and Fault Localization Based on Time–Frequency-Domain Reflectometry

In addition to diagnosing a wiring of the vehicle in operation, it is also very important to find wire mismatches during the assembly process. In this paper, we propose a new method combining time–frequency-domain reflectometry and deep learning to verify that the wire is connected to the proper port of the underhood electrical center. Considering the frequency characteristics of each wire (black, blue, red, and yellow), we develop an optimization signal design algorithm. Using the time–frequency cross correlation (TFCC), the reflected signal generated at the impedance discontinuities is acquired and converted into the Wigner–Ville distribution image. Through the proposed algorithm, the existing images are converted into new images, which are easy to distinguish among groups. The new images are used as input of the convolutional neural network and trained to learn the feature of each group. The lengths, compensation filters, and the port information to be connected to each wire are stored in the filter bank. If the distance derived using the TFCC is different from the stored length, the wire is considered defective, and the acquired signal is restored by the compensation filter designed by the overcomplete wavelet transform method. Experimental results demonstrate the effectiveness of the proposed method for detecting the wire mismatch and fault location.

Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium

The estimation of instantaneous frequency (IF) method is introduced to compensate for the group velocity of electromagnetic wave in dispersive medium. The location of the reflected signal can be obtained by using the time-frequency cross correlation (TFCC), following which it is used to extract the transmitted signal from the total signal acquired. The signal propagated in the dispersive medium is attenuated and distorted by the attenuation characteristics, which depend on the frequency of the medium. By using the IF curve calculated for the transmitted signal, the changed center frequency and time terms can be obtained. The obtained terms are used to compensate for the group velocity error induced by signal distortion and attenuation. Through experiments and simulation, the accuracy of the proposed method is 2% higher than that of the conventional method when the signal propagates over a long distance.

A Novel MIMO Channel Model for Vehicle-to-Vehicle Communication System on Narrow Curved-Road Environment

This paper presents a generalized statistical multiple-input multiple-output (MIMO) fading channel model for a vehicle-to-vehicle (V2V) non-line of sight mobile communication system on narrow curved-road environments. The MIMO channel model captures the propagation effect that occurs when vehicles move towards a junction with a side road and corner scatterings. In this paper, both single- and double-bounced scattering mechanisms are considered and it is assumed that the scatterers are uniformly distributed in the form of narrow arcs outside the annular road. Our geometry-based channel model takes the exact relationship between the angle-of-arrival and the angle-of-departure into account. Thereafter, the statistical properties of the model are studied under the assumption of non-isotropic scattering. Analytical expressions of different parameters, such as the space---time-frequency cross-correlation function, Doppler power spectral density, and MIMO channel capacity are provided. Furthermore, the influence of model parameters on the performance of V2V communication system has been analyzed. The study provides a design layout of MIMO V2V communication systems with a wide-band spatial channel model enabling the performance analysis of new high-data-rate transmission schemes for narrow curved-road propagations.

Multiple Chirp Reflectometry for Determination of Fault Direction and Localization in Live Branched Network Cables

This paper proposes a multiple inductive coupler chirp reflectometry system with multiple resolutions that can determine fault direction, and localize fault is proposed to apply the live target cable. The multiple resolution method enables us to detect and localize the faults, regardless of their location. Time delay information from multiple couplers, which is derived from the time-frequency cross correlation, indicates the fault direction and fault distance, even in cable branched network. The multiple inductive couplers system is proposed to obtain the velocity of propagation and fault direction in cable branch. Using the overcomplete wavelet transform, the signal restoration process can be carried out under signal distortion due to the propagation characteristics of the inductive coupler and target cable. In addition, combining notch filtering techniques and chirp reflectometry method is proposed to cancel 60-Hz harmonic noise, which is acquired from the actual substation. Through the experiments, impedance discontinuity localization and direction determination performance of the proposed method and the adaptability of the proposed method in live cable under harmonic noise by comparing with the existing method were verified.

Monitoring Electrical and Thermal Characteristics of HTS Cable Systems via Time–Frequency Domain Reflectometry

A high-temperature superconducting (HTS) cable system with the 22.9 kV, 50 MVA, and 410 m length is installed and operated at 154 kV Icheon substation of Korea Electric Power Corporation (KEPCO). Unfortunately, it is a difficult task to diagnose and monitor electrical and thermal characteristics of the HTS cable system in a real-time manner. In order to protect operational failures of grid-connected HTS cable systems, this paper proposes time-frequency domain reflectometry (TFDR) and analysis techniques, i.e., time-frequency cross correlation and instantaneous frequency estimation. To verify the performance of the proposed method, the temperature is changed via the cryogenic refrigeration system and the status of the grid-connected HTS cable is monitored via TFDR in a real-time manner.

The leak location package for assessment of the time-frequency correlation method for leak location

The paper describes the simplest implementation of a software and hardware package for acoustic correlation leak location and results of its performance assessment for location of water leaks from a metallic pipe in laboratory conditions. A distinctive feature of this leak locator is the use of the software based on the time-frequency correlation analysis of signals, which was proposed in our previous papers. Comparative analysis results are given for the information content of classical and time-frequency cross-correlation functions as obtained during processing of experimental data. The results obtained justify comparatively higher efficiency of a time-frequency cross correlation method to solve the leak location task. Improved efficiency is determined by bandpass filtration embedded into the time-frequency cross-correlation function calculation.

A Novel Wideband MIMO Car-to-Car Channel Model Based on a Geometrical Semi-Circular Tunnel Scattering Model

In this paper, we present a wideband multiple-input multiple-output (MIMO) car-to-car (C2C) channel model based on a geometrical semi-circular tunnel (SCT) scattering model. From the geometrical SCT scattering model, a reference channel model is derived under the assumption of single-bounce scattering in line-of-sight (LOS) and non-LOS (NLOS) propagation environments. In the proposed reference channel model, it is assumed that an infinite number of scatterers are randomly distributed on the tunnel wall. Starting from the geometrical scattering model, the time-variant transfer function (TVTF) is derived, and its correlation properties in time, frequency, and space are studied. Expressions are presented for the space–time–frequency cross-correlation function (STF-CCF), the two-dimensional (2D) space CCF, the 2D time–frequency CCF (TF-CCF), the temporal autocorrelation function (ACF), and the frequency correlation function (FCF). Owing to the semi-circular geometry, we reduced the originally threefold integrals to double integrals in the computations of the correlation functions, which simplifies the numerical analysis considerably. From the TVTF characterizing the reference model, an efficient sum-of-cisoid (SOC) channel simulator is derived. Numerical results show that both the temporal ACF and the FCF of the SOC channel simulator match very well with those of the reference model. A validation of the proposed model has been done by fitting the delay spread of the reference model to that of the measured channel, which demonstrates an excellent agreement. The proposed channel simulator allows us to evaluate the performance of C2C communication systems in tunnel environments.

Soft fault detection in cables using the cluster time-frequency domain reflectometry

Faced with the continual increase in complexity of wiring networks, their reliability, in particular in embedded fields (such as automotive and aerospace industries) becomes a major issue. In wiring diagnostics, reflectometry methods are commonly used. They are quite efficient for detecting important damage (hard faults) such as short-or open-circuits. These faults can have heavy material (e.g.: fires) and economical consequences. Then detecting them before they happen would be very interesting and useful. Unfortunately no current method is efficient enough for addressing soft faults. This article proposes an original approach to overcome this problem: the Cluster Time-Frequency Domain Reflectometry (CTFDR). Based on the fact that a wire is most often in a bundle with several other wires, this method takes advantage of the resultant near end crosstalk signals to get more information about the state of the wires in the bundle. To make the detection of incipient faults easier, a normalized time-frequency cross correlation function has also been used.

Diagnosis of cables in nuclear power plants using joint time-frequency domain reflectometry

In this paper, a new methodology which is referred as joint time-frequency domain reflectometry (JTFDR) is proposed to detect and to locate faults of control and instrumental cables in nuclear power plants. Since this method considers information both in time and frequency domain at the same time, we use a linearly increasing Gaussian envelope chirp reference signal as an incident signal which has a adjustable time duration and frequency bandwidth together. After calculating the time-frequency cross-correlation function with two distribution signals, we can determine the fault distance using time delay between the peak of the incident and reflected signals in the cross-correlation. The usefulness of the proposed method in nuclear power plants is verified by the experiments. Experimental results show that JTFDR can be the reliable method for diagnosis of control and instrumentation cables in the nuclear power plants.

Research on Aircraft Cable Defects Locating Method Based on Time-Frequency Domain Reflection

This paper presents an aircraft cable fault detection and location method based on TFDR in order to detect the intermittent faults effectively and to cope with the serial and after-connector faults being difficult to be detected in time domain reflection. In this method, the correlation function of reflected and reference signal is utilized to detect and locate the aircraft fault according to the characteristics of reflected and reference signal in time-frequency domain, so the hit rate of detecting and locating intermittent faults can be improved effectively. In the work process, the reflected signal is interfered by the noise and false alarm takes place frequently, so the threshold de-noising method based on wavelet decomposition is utilized to decrease the noise interference and reduce the fault alarm rate. Then the time-frequency cross correlation function of the reference signal and the reflected signal based on Wigner-Ville distribution is computed in order to locate the fault position. At last, LabVIEW is applied to implement operation and control interface, the main function of which is to link and control MATLAB and LABSQL. Utilizing the strong calculating capability and the abundant function library of MATLAB, the signal processing turn to be easily realized, moreover LabVIEW help the system to be more reliable and updated easily.

Detection and Location of Multiple Wiring Faults via Time–Frequency-Domain Reflectometry

In this paper, we propose a high-resolution time-frequency-domain reflectometry technique as a methodology of detection and estimation of faults on a wire. This method adopts the time-frequency cross-correlation characteristics of the observed signal in both the time and frequency domains simultaneously. The accuracy of the proposed method is verified with experiments using a radio-guide-type coaxial cable and comparing it with traditional time-domain as well as frequency-domain reflectometry methods. It is clearly shown here that the proposed algorithm produces excellent results compared to the conventional methods for single as well as multiple fault cables.

Sweep signal time-delay estimation with time–frequency cross-correlation algorithm

Sweep signal is usually employed as a source signal in active detection such as radar and sonar. Since the frequency spectrum of the sweep signal varies against time, a novel algorithm, namely a time–frequency cross-correlation (TFCC) algorithm based on wavelet packet transform (WPT), is proposed to estimate the time delay of sweep signal. In this algorithm, the source sweep and the received signals are decomposed with WPT to obtain their time–frequency representations and the TFCC between the source sweep and the received signals is performed. Each reflected sweep in the received signal is converted into a time–frequency correlation peak whose position can indicate its time delay. The TFCC algorithm can suppress ambient noise effectively and improve the performance of sweep extraction and can match more precisely the source and the reflected sweeps to their known time–frequency characters. Numerical experiments were performed to compare the performance of the TFCC algorithm with that of the conventional cross-correlation and phase-data algorithms. The results proved that the TFCC algorithm can extract the reflected sweeps effectively and its performance is better than that of the conventional algorithms.

Extracting reflection with wavelet transform in vibroseis signal processing

In conventional vibroseis signal processing, algorithms including cross correlation and deconvolution are applied to convert the raw trace data into a seismic section. However, their performance deteriorates when the trace data are corrupted by the ambient noise, so the mathematical tool for time–frequency analysis and wavelet transform is applied in this paper to overcome the difficulty. A time–frequency cross correlation (TFCC) algorithm based on wavelet transform is proposed to extract the reflection from the trace data by detecting the reflected sweeps and estimating their time delay. The source signal and the trace data are transformed into time–frequency domain with respect to a same wavelet basis function; then time–frequency cross correlation is performed between the source signal and the trace data. The reflected sweeps are converted into time–frequency correlation wavelets in the result; meanwhile, the trace data are converted into seismic section. In wavelet decomposition, the high-frequency noise can be suppressed automatically. In the time–frequency representation of the trace data, the ambient noise and the reflected sweeps can be separated from each other. So in the TFCC algorithm, the interference of the ambient noise can be decreased considerably, and the weak reflections can be extracted clearly. Real vibroseis trace data were processed with the TFCC algorithm and the conventional cross correlation. The results showed the superiority of the proposed new algorithm in vibroseis signal processing.

Application of Time-Frequency Domain Reflectometry for Detection and Localization of a Fault on a Coaxial Cable

In this paper, we introduce a new high-resolution reflectometry technique that operates simultaneously in both the time and frequency domains. The approach rests upon time-frequency signal analysis and utilizes a chirp signal multiplied by a Gaussian time envelope. The Gaussian envelope provides time localization, while the chirp allows one to excite the system under test with a swept sinewave covering a frequency band of interest. This latter capability is of particular interest when testing communication cables and systems. Sensitivity in detecting the reflected signal is provided by a time-frequency cross-correlation function. The approach is verified by experimentally locating various types of faults, located at various distances, in RG 142 and RG 400 coaxial cables.