Engineering-Driven Wavelet Analysis of Cyclic Functional Data for Multiple Embedded Operations Diagnosis

用于多嵌入式操作诊断的循环函数数据的工程驱动小波分析

• 批准号: 0500176
• 负责人: Jionghua (Judy) Jin
• 金额: --
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-07-01 至 2005-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0500176&HistoricalAwards=false
• 关键词: Engineering; Driven; Wavelet; Analysis; Cyclic

This grant provides funding for developing systematic methodologies for effectively analyzing, monitoring, and inferring of cyclic functional sensing data for multiple embedded operation diagnosis in complex processes. The proposed functional data analysis methodology will be developed through the unique fusion of engineering knowledge with statistical wavelet analysis for data dimension reduction, data segmentation, feature extraction, and root causes diagnosis. The research will be conducted through (a) systematic (rather than subjective) data segmentation through process-oriented change-point detection; (b) engineering-driven wavelet thresholding (rather than only data-denoising) for efficient feature preserving data dimension reduction; (c) multiscale diagnostic mapping algorithms for effective feature extraction and root cause diagnosis (rather than only monitoring); and (d) optimal (rather than trial-and-error) wavelet basis selection to match with specific data characteristics and data analysis objectives. If successful, the results of this research will lead to improvements of the existing statistical process control (SPC) technology by adding a new set of monitoring and diagnostic tools for high dimensional functional data to achieve better process control and quality improvement. The implementation of the proposed methodology will facilitate the development of effective monitoring and diagnostic systems to significantly reduce process downtime and manufacturing cost, thus leading to significant benefits to the national economy. The proposed work will also extensively enhance the research and broaden the application domains of wavelet analysis to achieve better functional data analysis for diagnostic purposes. Meanwhile, it will contribute to the creation of a new workforce with multidisciplinary skills through new curriculum developments, involvement of undergraduate/K-12 students in the research activities, and close industrial collaborations.

该补助金为开发有效分析，监测和推断复杂过程中多个嵌入式操作诊断的循环功能传感数据的系统方法提供资金。拟议的功能数据分析方法将开发通过独特的融合工程知识与统计小波分析数据降维，数据分割，特征提取，和根本原因诊断。该研究将通过以下方式进行：（a）系统地通过面向过程的变点检测进行（而非主观的）数据分割;（B）工程驱动的小波阈值化（而不仅仅是数据去噪），用于有效的特征保留数据降维;（c）多尺度诊断映射算法，用于有效的特征提取和根本原因诊断（而不是仅仅监测）;（d）最佳（而不是试错法）小波基选择，以符合具体的数据特征和数据分析目标。如果成功的话，这项研究的结果将导致现有的统计过程控制（SPC）技术的改进，增加了一套新的监测和诊断工具，高维功能数据，以实现更好的过程控制和质量改进。拟议方法的实施将有助于开发有效的监测和诊断系统，以大大减少工艺停机时间和制造成本，从而为国民经济带来重大利益。本论文的工作将进一步拓展小波分析的研究和应用领域，以更好地实现功能数据的诊断分析。与此同时，它将有助于通过新的课程开发，本科生/K-12学生参与研究活动以及密切的工业合作，创造具有多学科技能的新劳动力。