I-Corps: Method and Tool for Time-Domain Processing of Densely Sampled Signals

这个I-Corps项目的更广泛/商业潜力将主要在半导体制造业。该行业迫切需要一种工具，用于从大量密集采样的传感器读数中提取有用的信息。这种需要特别强调的是，最近急剧增加的数量，可用的传感器读数，以及采样率，这些读数被收集。该工具预计将在该行业中发挥双重作用：提供信息访问以实现决策，并作为减少信息丢失的数据简化工具。此外，预计这种工具在使用非旋转设备的任何行业中都有用，包括石油和天然气开采、炼油厂、化学加工行业等。预计在许多这样的行业中收集的数据量将继续增加，进一步激发了开发和投资这种特征提取和数据简化工具的需求。Corps项目将侧重于探索和提高技术创新的潜力，这将使非旋转设备和过程产生的信号能够完全自动化地提取信息特征。这样的工具将能够将原始数据减少到在大小上显著小于原始信号的特征集合中，从而使得能够在大量密集采样的传感器信号中显著更容易地进行数据管理和数据挖掘。该方法的基础上，这个工具是最近开发的。它首先将信号分割成一系列稳态和瞬态部分，然后使用每个数据段来构建许多基于几何学和基于动态的签名。最终目标是创建一个工具，与目前使用的需要大量人工输入和分析的方法相比，该工具将在最少或希望没有人工参与的情况下实现所有这一切。

The broader/commercial potential of this I-Corps project will be primarily in the semiconductor manufacturing industry. This industry desperately needs a tool for extraction of useful information from a large number of densely sampled sensor readings. This need is especially emphasized by the recent dramatic increases both in the number of available sensor readings, as well as the sampling rates at which those readings are collected. This tool is expected to serve a dual purpose in this industry; providing access to information to enable decision making and serve as a data reduction tool which reduces information loss. Furthermore, such a tool is expected to be useful in any industry that employs non-rotating equipment, including the oil and gas extraction, refineries, chemical process industry etc. It is expected that the volume of data gathered in many such industries will continue to increase, further motivating the need to develop and invest in such feature extraction and data reduction tools.This I-Corps project will focus on exploring and improving potentials of a technological innovation that will enable a fully automated extraction of informative features from signals generated by non-rotating equipment and processes. Such a tool will be able to reduce the original data into a feature set that is significantly smaller in size than the original signal, thus enabling a significantly easier data management and data mining in a large number of densely sampled sensor signals. The method in the foundation of this tool has recently been developed. It starts by segmenting the signal into a series of steady-state and transient portions, after which each segment of the data is used to construct a number of statistics-based and dynamics based signatures. The ultimate goal is to create a tool that will achieve all this with minimal or, hopefully no human involvement, in contrast to the currently used approaches, which require extensive manual inputs and analyses.