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

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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.