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Multi-resolution approach to identification of recurring patterns in process signal.

机译：用于识别过程信号中重复模式的多分辨率方法。

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摘要

Process control is important for enhancing the quality of manufactured products. Manufacturing processes are generally monitored by observing uniformly sampled process signals collected from application specific sensors and comparing them against known standard patterns. Effective process monitoring and control requires identification of different types of variation, including recurring patterns, in process variables. From the process control view point, any repeating patterns in the process measurements will warrant an investigation into potentially assignable causes. In order to devise an effective process control scheme, a novel universally applicable method for the identification of the repeated occurrence of patterns in process measurements is described in this thesis. First the sampled process signal is decomposed into signals of different resolution using a trous translation invariant wavelet transform. Next, a frequency index is assigned to every sampling point of the process signal at every resolution level to improve the pattern recognition. Recurring patterns detected at different resolutions using neighborhood search in Euclidian space. The experimental results show that the method used in this work accurately detects a broader family of recurring patterns even in the presence of noise.
机译：过程控制对于提高制成品的质量很重要。通常，通过观察从专用传感器收集的均匀采样的过程信号并将其与已知的标准模式进行比较来监控制造过程。有效的过程监视和控制需要识别过程变量中不同类型的变化，包括重复发生的模式。从过程控制的角度来看，过程测量中的任何重复模式都将需要对可能的可归因进行调查。为了设计一种有效的过程控制方案，本文描述了一种新颖的通用方法，用于识别过程测量中图案的重复出现。首先，使用平移不变小波变换将采样的过程信号分解为不同分辨率的信号。接下来，将频率索引分配给每个分辨率级别的过程信号的每个采样点，以改善模式识别。使用欧几里得空间中的邻域搜索以不同分辨率检测到的重复模式。实验结果表明，即使在存在噪声的情况下，这项工作中使用的方法也可以准确地检测到更大范围的重复模式。

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