NDLI: Intelligent acoustic detection of defective porcelain station post insulators

Content Provider	IEEE Xplore Digital Library
Author	Lasalvia, L.A.B. Florentine, M.T.B. Ferreira, T.V. Germano, A.D. da Costa, E.G.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Fed. Univ. of Campina Grande, Campina Grande, Brazil (Florentine, M.T.B.; Ferreira, T.V.; Germano, A.D.; da Costa, E.G.) \|\| Dept. of Substation Maintenance, Sao Francisco's Hydroelectric Co., Recife, Brazil (Lasalvia, L.A.B.)
Abstract	This work presents an intelligent acoustic methodology for detection of defective porcelain station post insulators, which are widely used in substations in the form of column presenting several sheds. The acoustic emission inspection aims to detect cracks or fissures in a particular shed, which will have its insulating capacity severely decreased, if cracked. The test is done by gently striking the shed with an appropriate instrument, connected to the tip of an insulated pole. The resulting acoustic emissions are recorded at the substation. A database is created with these audio files and two approaches are considered in order to emphasize the important attributes and to compact the information: Wavelet Energy Coefficients and Spectral Subband Centroid Energy Vectors. Finally, to add reliability, automation and ability to generalize and to adapt to new situations, an Artificial Neural Network is employed. The average classification accuracy is above 62% when using Wavelet Energy Coefficients and above 98% when using Spectral Subband Centroid Energy Vectors.
Starting Page	118
Ending Page	122
File Size	912551
Page Count	5
File Format	PDF
ISBN	9781479973521
e-ISBN	9781479973545
DOI	10.1109/ICACACT.2014.7223500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Acoustics Reliability Substations Topology Training wavelet Acoustic emissions artificial neural networks diagnostic frequency spectrum station post insulators
Content Type	Text
Resource Type	Article

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