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

这笔赠款为开发系统方法提供资金，以有效分析、监测和推断循环功能传感数据，用于复杂过程中的多个嵌入式操作诊断。建议的功能性数据分析方法将通过工程知识与统计小波分析的独特融合来开发，用于数据降维、数据分割、特征提取和根本原因诊断。研究将通过：(A)通过面向过程的变点检测进行系统的(而不是主观的)数据分割；(B)工程驱动的小波阈值(而不仅仅是数据去噪)，以有效地保持数据的特征降维；(C)用于有效的特征提取和根本原因诊断(而不仅仅是监测)的多尺度诊断映射算法；以及(D)最优(而不是反复尝试)的小波基选择，以匹配特定的数据特征和数据分析目标。如果成功，这项研究的结果将通过增加一套新的高维功能数据监测和诊断工具来改进现有的统计过程控制(SPC)技术，以实现更好的过程控制和质量改进。拟议方法的实施将促进开发有效的监测和诊断系统，以显著减少工艺停机时间和制造成本，从而为国民经济带来重大好处。建议的工作还将广泛加强小波分析的研究和拓宽应用领域，以实现更好的诊断功能数据分析。同时，它将通过开发新的课程，让本科生/K-12学生参与研究活动，以及密切的行业合作，帮助建立一支具有多学科技能的新劳动力队伍。

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.