PREVENTING FAILURES BY MINING MAINTENANCE LOGS WITH CASE-BASED REASONING

机译：通过基于案例的推理挖掘维护日志来预防故障

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The project integrates work in natural language processing, machine learning, and the semantic web, bringing together these diverse disciplines in a novel way to address a real problem. The objective is to extract and categorize machine components and subsystems and their associated failures using a novel approach that combines text analysis, unsupervised text clustering, and domain models. Through industrial partnerships, this project will demonstrate effectiveness of the proposed approach with actual industry data.

机译：该项目将自然语言处理，机器学习和语义网中的工作集成在一起，以新颖的方式将这些不同的学科整合在一起，以解决实际问题。目的是使用一种结合了文本分析，无监督文本聚类和领域模型的新颖方法，对机器组件和子系统及其相关的故障进行提取和分类。通过行业合作，该项目将通过实际的行业数据证明该方法的有效性。

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来源
《Society for Machinery Failure Prevention Technology Meeting; 20050418-21; Virginia Beach,VA(US)》|2005年|P.369-378|共10页
会议地点 Virginia BeachVA(US)
作者
Mark Devaney; Ashwin Ram; Hai Qiu; Jay Lee;
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作者单位

Enkia Corporation;

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会议组织
原文格式 PDF
正文语种 eng
中图分类 TH171;
关键词
data mining; semantic web; maintenance log analysis; natural language processing; machine learning; case-based reasoning;

机译：数据挖掘;语义网;维护日志分析;自然语言处理;机器学习;基于案例的推理;

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PREVENTING FAILURES BY MINING MAINTENANCE LOGS WITH CASE-BASED REASONING

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