Engineering Faculty Internship: Intelligent Machine Tool Monitoring: A Neural Network Approach

工程学院实习：智能机床监控：神经网络方法

• 批准号: 9113770
• 负责人: Hsu-Pin (Ben) Wang
• 金额: $ 1.71万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-01 至 1993-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9113770&HistoricalAwards=false
• 关键词: Engineering; Faculty; Internship; Intelligent; Machine

This research will extend and augment previous work on knowledge-based machine fault diagnosis, using artificial neural networks. The starting point of this effort will be the framework of previous machine monitoring research. Analytical modeling of behavioral phenomenon will be carried out to establish quantitative and qualititative relationships of spatial considerations and fault diagnosis models. This modeling will be interfaced to the real world to carry out intelligent monitoring. Intelligent monitoring of machine tools for diagnostic purposes is of great importance in automated manufacturing environments. This work is being conducted in close cooperation with a major manufacturing organization and is likely to yield important results.

本研究将利用人工神经网络扩展和增强以往基于知识的机器故障诊断工作。这项工作的出发点将是以前机器监测研究的框架。将对行为现象进行分析建模，建立空间考虑和故障诊断模型的定量和定性关系。该建模将与现实世界接口，以进行智能监控。在自动化制造环境中，以诊断为目的的机床智能监测非常重要。这项工作正在与一个主要制造组织密切合作进行，很可能产生重要成果。