预测和智能制造中考虑的问题构建方法综述

IF 1.4 Q2 ENGINEERING, MULTIDISCIPLINARY
Patrick T. Hester, Andrew J. Collins, B. Ezell, J. Horst
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引用次数: 1

摘要

预测的成功使用涉及对未来系统行为的预测,以努力保持系统可用性并降低维护和维修成本。美国国家标准与技术研究所最近的工作表明,预测和健康管理领域对于在当今制造环境中保持竞争力至关重要。尽管基于预测的维护涉及到许多传统的以作战研究为中心的成功部署挑战,如信息可用性有限和对计算效率的担忧,但作者在本文中认为,预测和健康管理领域仍处于萌芽发展阶段,也可以从考虑软作战研究技术中受益匪浅。具体而言,作者建议使用定性问题结构化技术来帮助理解和界定问题。本文概述了这些软方法,并讨论和演示了制造商如何使用它们。将问题构建方法与传统运筹学技术相结合的方法将有助于加速预测领域的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Review of Problem Structuring Methods for Consideration in Prognostics and Smart Manufacturing
Successful use of prognostics involves the prediction of future system behaviors in an effort to maintain system availability and reduce the cost of maintenance and repairs. Recent work by the National Institute of Standards and Technology indicates that the field of prognostics and health management is vital for remaining competitive in today’s manufacturing environment. While prognostics-based maintenance involves many traditional operations researchcentric challenges for successful deployment such as limited availability of information and concerns regarding computational efficiency, the authors argue in this paper that the field of prognostics and health management, still in its embryonic development stage, could benefit greatly from considering soft operations research techniques as well. Specifically, the authors propose the use of qualitative problem structuring techniques that aid in problem understanding and scoping. This paper provides an overview of these soft methods and discusses and demonstrates how manufacturers might use them. An approach combining problem structuring methods with traditional operations research techniques would help accelerate the development of the prognostics field.
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来源期刊
CiteScore
2.90
自引率
9.50%
发文量
18
审稿时长
9 weeks
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