Sensors: Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing

传感器：用于自动化制造的智能多传感器建模、识别和数据融合

• 批准号: 0427597
• 负责人: Devendra Garg
• 金额: --
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-10-01 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0427597&HistoricalAwards=false
• 关键词: Sensors; Intelligent; Multi; Sensor; Modeling

Intelligent Multi-Sensor Modeling, Identification, and Data Fusion for Automated Manufacturing(NSF Proposal No. 0427597)AbstractThe primary goal of this research effort is to develop innovative methodologies for modeling, identifying and fusing data from multiple heterogeneous information sources/sensors in a flexible manufacturing workcell environment. Major contributions resulting from this research would consist of integrating artificial intelligence techniques, such as Genetic-Neuro-Fuzzy method and Bayesian estimation, with elements of signal processing and statistical techniques such as Extended Kalman filtering, and parametric estimation methods such as Maximum Likelihood, and Expectation Maximization. This research project would focus on the following three major components: 1) precise sensor modeling which would include obtaining analytical and probabilistic models of sensors and associated noises, understanding their capabilities and limitations, 2) extracting real-time data from multiple sensors and interpreting the data on a common processing platform, and 3) developing strategies to combine the noise-free data from these sensors to remove uncertainty and to obtain accurate model of the environment the manufacturing system operates in. The innovative theories developed would be tested and validated via experiments conducted at Duke University's flexible manufacturing workcell. Since the research would emphasize the use of a variety of sensors in manufacturing processes, the manufacturing industry would be a major beneficiary.

智能多传感器建模，识别和数据融合的自动化制造（NSF提案号0427597）AbstractThe主要目标，这项研究工作是开发创新的方法建模，识别和融合数据从多个异构的信息源/传感器在一个灵活的制造工作单元环境。本研究的主要贡献包括将人工智能技术（如遗传神经模糊方法和贝叶斯估计）与信号处理和统计技术（如扩展卡尔曼滤波）以及参数估计方法（如最大似然法和期望最大化）相结合。该研究项目将侧重于以下三个主要组成部分：1）精确的传感器建模，包括获得传感器和相关噪声的分析和概率模型，了解它们的能力和局限性，2）从多个传感器提取实时数据并在公共处理平台上解释数据，以及3）开发策略以将来自这些传感器的无噪声数据联合收割机组合，以消除不确定性并获得制造系统操作环境的精确模型。开发的创新理论将通过在杜克大学的柔性制造车间进行的实验进行测试和验证。由于该研究将强调在制造过程中使用各种传感器，制造业将成为主要受益者。