Whither AI in test and diagnosis?

被引：1
作者：
Fenton, B
McGinnity, TM
Maguire, LP
机构：
来源：
IEEE SYSTEMS READINESS TECHNOLOGY CONFERENCE: 2001 IEEE AUTOTESTCON PROCEEDINGS | 2001年
关键词：
fault diagnosis; artificial intelligence; rule-based reasoning; model-based reasoning; diagnostic inference models; case-based reasoning; machine learning; fuzzy logic; neural networks;
D O I：
10.1109/AUTEST.2001.948979
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
In an increasingly competitive marketplace system complexity continues to grow, but time-to-market and lifecycle are reducing. This has driven the need for automated test and diagnostic tools. As test and diagnosis is a high-level human activity, Al-based solutions have been pursued. This has been an active research area for some decades, but the industrial acceptance of Al approaches, particularly in cost-sensitive areas, has not been high. This paper reviews the history and current state of Al in test and diagnosis, discusses future challenges, and introduces the authors' work in addressing some of these challenges in a diagnostic context.
引用
收藏
页码：333 / 351
页数：19
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