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 2575Full text not available from this repository. (Request a copy)
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.
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