Acoustic emission signal classification using fuzzy c-means clustering

Fuzzy c-means (FCM) clustering is used to classify the acoustic emission (AE) signal to different sources of signals. FCM has the ability to discover the cluster among the data, even when the boundaries between the subgroup are overlapping, FCM based technique has an advantage over conventional stat...

Full description

Saved in:
Bibliographic Details
Main Authors: Omkar, S.N., Suresh, S., Raghavendra, T.R., Mani, V.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic emission
Acoustic materials
Acoustic noise
Nearest neighbor searches
Neural networks
Pattern classification
Signal analysis
Signal processing
Sparks
Testing
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fuzzy c-means (FCM) clustering is used to classify the acoustic emission (AE) signal to different sources of signals. FCM has the ability to discover the cluster among the data, even when the boundaries between the subgroup are overlapping, FCM based technique has an advantage over conventional statistical technique like maximum likelihood estimate, nearest neighbor classifier etc, because they are distribution free (i.e.) no knowledge is required about the distribution of data. AE test is carried out using pulse, pencil and spark signal source on the surface of solid steel block. Four parameters-event duration (E/sub d/), peak amplitude (P/sub a/), rise time (R/sub t/) and ring down count (R/sub d/) are measured using AET 5000 system. These data are used to train and validate the FCM based classification.
DOI:10.1109/ICONIP.2002.1198989