Data quality enhancement and knowledge discovery from relevant signals in acoustic emission

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The increasing popularity of structural health monitoring has brought with it a growing need for automated data management and data analysis tools. Of great importance are filters that can systematically detect unwanted signals in acoustic emission datasets. This study presents a semi-supervised data mining scheme that detects data belonging to unfamiliar distributions. This type of outlier detection scheme is useful detecting the presence of new acoustic emission sources, given a training dataset of unwanted signals. In addition to classifying new observations (herein referred to as "outliers") within a dataset, the scheme generates a decision tree that classifies sub-clusters within the outlier context set. The obtained tree can be interpreted as a series of characterization rules for newly-observed data, and they can potentially describe the basic structure of different modes within the outlier distribution. The data mining scheme is first validated on a synthetic dataset, and an attempt is made to confirm the algorithms' ability to discriminate outlier acoustic emission sources from a controlled pencil-lead-break experiment. Finally, the scheme is applied to data from two fatigue crack-growth steel specimens, where it is shown that extracted rules can adequately describe crack-growth related acoustic emission sources while filtering out background "noise." Results show promising performance in filter generation, thereby allowing analysts to extract, characterize, and focus only on meaningful signals.

Original languageEnglish (US)
Pages (from-to)381-394
Number of pages14
JournalMechanical Systems and Signal Processing
Volume62
DOIs
StatePublished - Oct 1 2015

Keywords

  • Acoustic emission
  • Bridges
  • Data mining
  • Non-destructive testing
  • Structural health monitoring

ASJC Scopus subject areas

  • Mechanical Engineering
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Signal Processing

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