65,838 research outputs found
Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features
The detection and characterization of partial discharge (PD) are crucial for
the insulation diagnosis of overhead lines with covered conductors. With the
release of a large dataset containing thousands of naturally obtained
high-frequency voltage signals, data-driven analysis of fault-related PD
patterns on an unprecedented scale becomes viable. The high diversity of PD
patterns and background noise interferences motivates us to design an
innovative pulse shape characterization method based on clustering techniques,
which can dynamically identify a set of representative PD-related pulses.
Capitalizing on those pulses as referential patterns, we construct insightful
features and develop a novel machine learning model with a superior detection
performance for early-stage covered conductor faults. The presented model
outperforms the winning model in a Kaggle competition and provides the
state-of-the-art solution to detect real-time disturbances in the field.Comment: To be published in IEEE Transactions on Smart Gri
Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples
Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry. View Full-TextPeer ReviewedPostprint (published version
Methods for Reducing False Alarms in Searches for Compact Binary Coalescences in LIGO Data
The LIGO detectors are sensitive to a variety of noise transients of
non-astrophysical origin. Instrumental glitches and environmental disturbances
increase the false alarm rate in the searches for gravitational waves. Using
times already identified when the interferometers produced data of questionable
quality, or when the channels that monitor the interferometer indicated
non-stationarity, we have developed techniques to safely and effectively veto
false triggers from the compact binary coalescences (CBCs) search pipeline
Classification methods for noise transients in advanced gravitational-wave detectors
Noise of non-astrophysical origin will contaminate science data taken by the
Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and
Advanced Virgo gravitational-wave detectors. Prompt characterization of
instrumental and environmental noise transients will be critical for improving
the sensitivity of the advanced detectors in the upcoming science runs. During
the science runs of the initial gravitational-wave detectors, noise transients
were manually classified by visually examining the time-frequency scan of each
event. Here, we present three new algorithms designed for the automatic
classification of noise transients in advanced detectors. Two of these
algorithms are based on Principal Component Analysis. They are Principal
Component Analysis for Transients (PCAT), and an adaptation of LALInference
Burst (LIB). The third algorithm is a combination of an event generator called
Wavelet Detection Filter (WDF) and machine learning techniques for
classification. We test these algorithms on simulated data sets, and we show
their ability to automatically classify transients by frequency, SNR and
waveform morphology
Methods for detection and characterization of signals in noisy data with the Hilbert-Huang Transform
The Hilbert-Huang Transform is a novel, adaptive approach to time series
analysis that does not make assumptions about the data form. Its adaptive,
local character allows the decomposition of non-stationary signals with
hightime-frequency resolution but also renders it susceptible to degradation
from noise. We show that complementing the HHT with techniques such as
zero-phase filtering, kernel density estimation and Fourier analysis allows it
to be used effectively to detect and characterize signals with low signal to
noise ratio.Comment: submitted to PRD, 10 pages, 9 figures in colo
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