CAREER: Process Monitoring, Identification, and Diagnosis Using Image and Functional Data

这个职业项目将建立一个教育和研究计划，利用高维功能数据或图像传感器信号进行过程监控、故障识别和故障诊断的方法学。由于图像和功能数据的海量和高维，及时获取综合信息以提高质量所需的分析和处理变得复杂和资源密集型。所提出的方法将结合基于小波的信号处理技术和面向数据挖掘的系统建模过程来开发制造过程中(或现场)控制工具。所提出的方法包括：(1)针对功能数据或图像传感器信号的自适应多尺度监测模型，以克服现有技术存在的故障检测概率低的问题；(2)基于核的故障识别指标度量和变量选择算法，以识别有助于提高诊断性能的过程变量；(3)非线性故障诊断模型，以确定故障的可分配原因，并有效地处理不适定的自相关功能数据或图像传感器信号。该方法的优点在于它是通用的，可适用于其他研究和应用领域，如在非线性轮廓监测、大规模传感器故障检测问题、光谱数据变量选择问题，或在生物过程、半导体和汽车制造以及其他制造过程中与非线性多变量统计过程控制相关的新研究问题的开发中。

This CAREER project will build an education and research program in methodology for process monitoring, fault identification, and fault diagnosis using high-dimensional functional data or image sensor signals. Due to the massive amount and high dimensionality of image and functional data, the analysis and manipulation required to obtain timely synthesized information for quality improvement becomes complicated and resource intensive. The proposed methods will integrate wavelet-based signal-processing techniques and system-modeling procedures oriented to data mining to develop an in-process (or in-situ) control tool for manufacturing processes. The proposed methodology includes the following: (1) an adaptive multi-scale monitoring model for functional data or image sensor signals to overcome the problem of the low fault detection probability present in existing techniques; (2) a kernel-based fault identification index measure and a variable selection algorithm to identify contributing process variables to improve diagnostic performance; (3) a nonlinear fault diagnosis model to determine an assignable cause for a fault and to effectively handle ill-posed autocorrelated functional data or image sensor signals.The results of this research will provide manufacturers with new tools for handling high-dimensional functional data or image sensor signals for quality improvement of their manufacturing processes. The strength of the proposed methodology is that it is generic and can be adapted to other research and application areas, such as in the monitoring of nonlinear profiles, the problem of large-scale sensor failure detection, and variable selection problems of spectrum data, or in the development of new research issues related to nonlinear multivariate SPC in bioprocesses, semiconductor and automobile manufacturing, and other manufacturing processes.