Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing
Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing
复制标题
用于自动解释无损检测数据的人工智能技术
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
N. Gupta
中科院分区：
文献类型：
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作者：
Siril Yella;M. Dougherty;N. Gupta
本文试图总结大量研究论文的结果，这些研究论文部署了人工智能（AI）技术，用于自动解释无损检测（NDT）数据。主要讨论了铁路运输领域的问题。然而，在本文中的大部分重点放在铁路检查的问题，因为它被认为是审查将提供一个完美的理由，在追求进一步的工作，在铁路检查领域的作者。NDT是一个广泛的名称，涉及材料和结构的均匀性，质量和适用性的各个方面，而不会对被检查的材料造成损坏的各种方法和程序。在过去的几年中，有关自动解释数据的NDT的问题已经得到了很好的关注，并激发了其他领域的兴趣，如交通，在其一些主题领域内进行关键评估。铁路、航空和海洋工业以及桥梁检查和路面维修都是这些领域的好例子，在这些领域已经做了大量的工作。此类工作巧妙地分为两个流派。第一个学派研究由经验丰富的操作人员使用数据来确定被检查材料或结构的状况。另一个学派则侧重于使用人工智能技术对NDT数据进行自动解释，以确定检查结果。本文的范围仅限于NDT数据的自动解释，其目标是以具有成本效益的方式尽可能快速准确地评估嵌入式缺陷。人工智能技术，如神经网络，机器视觉，基于知识的系统和模糊逻辑被应用于该领域的广泛问题。第二个目标是提供一个洞察可能的研究方法，铁路轨枕检测数据的自动解释。为不熟悉这些技术的读者提供了一个简短的介绍。
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.