Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing.

Yella, Siril, Dougherty, Mark S and Gupta, Naren K (2006) Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing. Insight - Non-Destructive Testing and Condition Monitoring, 48 (1). pp. 10-20. ISSN 1354 2575

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This paper attempts to summarise the findings of a large
number of research papers deploying artificial intelligence
(AI) techniques for the automatic interpretation of data from non-destructive testing (NDT). Problems in the rail transport domain are mainly discussed. However, a majority of the emphasis in this paper is laid on rail inspection problems, since it was believed that the review would provide a perfect ground to the authors in pursuing further work within the rail inspection area.

NDT is a broad name for a variety of methods and
procedures concerned with all aspects of uniformity, quality and serviceability of materials and structures, without causing damage to the material that is being inspected.

During the past several years, problems concerning the
automatic interpretation of data from NDT have received
good attention and have stimulated interests in other areas
like transportation, for making key assessments within some
of its subject areas. Rail, air and marine industries together with bridge inspection and pavement maintenance are good examples of such areas where a considerable amount of work has been done. Such work neatly splits into two schools. The first school investigates the classical usage of data by an experienced human operator to determine the condition of the inspected material or structure. The other school focuses attention on the automatic interpretation of NDT data using AI techniques, in determining the result of inspection.

The scope of this paper is only limited to the automatic
interpretation of data from NDT, with the goal of assessing
embedded flaws as quickly and accurately as possible in a
cost effective fashion. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in the area.

A secondary goal was to provide an insight into possible
research methods concerning railway sleeper inspection
by automatic interpretation of data. A brief introduction is provided for the benefit of the readers unfamiliar with the techniques.

Item Type: Article
Print ISSN: 1354 2575
Uncontrolled Keywords: Computer programming; Artificial intelligence; Non-destructive testing; Railway lines; Data interpretation; Pattern recognition; Neural networks; Machine vision; Knowledge-based systems; Fuzzy logic;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Engineering and the Built Environment
Dewey Decimal Subjects: 600 Technology > 620 Engineering > 625 Engineering of railroads & roads
000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods
600 Technology > 620 Engineering > 620 Engineering & allied operations
Library of Congress Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TF Railroad engineering and operation
Q Science > QA Mathematics > QA76 Computer software
Item ID: 1865
Depositing User: RAE Import
Date Deposited: 15 May 2008 16:10
Last Modified: 21 Mar 2013 14:31

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