System for extraction of representative data for training of adaptive process monitoring equipment
First Claim
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1. A method of selecting input vectors for extraction of representative data for training of an adaptive model, comprising:
- receiving signals as input from a plurality of sensors as a set of training vectors;
ordering the set of training vectors according to a corresponding value in each vector of a particular sensor;
dividing the set of training vectors according to equally spaced ranges according to the ordering; and
selecting at least one vector from each of the equally spaced ranges for training the adaptive model.
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Abstract
System and method for selection of appropriate modeling data from a general data set to characterize a modeled process. The data is typically correlated sensor data, representing a multitude of snapshots of a sensed machine or process. The invention accommodates selection of greater amounts of general data for inclusion in the modeling data where that data exhibits greater dynamics, and selects less data from regions of little change. The system can comprise a computer running a software program, or a microprocessor.
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Citations
49 Claims
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1. A method of selecting input vectors for extraction of representative data for training of an adaptive model, comprising:
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receiving signals as input from a plurality of sensors as a set of training vectors;
ordering the set of training vectors according to a corresponding value in each vector of a particular sensor;
dividing the set of training vectors according to equally spaced ranges according to the ordering; and
selecting at least one vector from each of the equally spaced ranges for training the adaptive model. - View Dependent Claims (2, 3, 4, 5, 6, 7, 33, 35, 37, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49)
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8. An adaptive apparatus for monitoring a system instrumented with sensors, comprising:
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data acquisition means for acquiring signals from sensors representative of operational states of the system;
an empirical modeling module responsive to the data acquisition means for providing indications about the operational states of the system;
a data store for storing modeling parameters for use by the empirical modeling module; and
a training module disposed to distill characteristic operational sensor data acquired from the system to a representative set of sensor data for storing in the data store, by selecting from the characteristic operational sensor data time-correlated observations representative of regularly spaced intervals along an ordering of the observations according to values in the observations of a particular sensor. - View Dependent Claims (9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31)
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13. A method of selecting a set of training vectors representative of an adaptive system, said training set forming an empirical model of said system, said method comprising the steps of
a) collecting historical data, said historical data including a plurality of system vectors each indicating an operating state of said system; -
b) selecting a system parameter in said system vector space;
c) ordering plurality of system vectors;
d) binning vector space for said selected parameter; and
e) selecting a vector from each bin;
f) selected said vectors forming a training set said training set forming said empirical model for monitoring system operation.
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26. A system for monitoring activity of another system, said system comprising:
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a control unit controlling a monitored system;
a data acquisition unit receiving information from said control unit and from said monitored system and providing system snapshots therefrom, system snapshots representing the state of said monitored system relative to the time the snapshot is taken;
a memory storing said system snapshots;
a sorter sorting collected system snapshots responsive to a selected system parameter; and
a vector selector binning sorted snapshots and selecting a vector from each bin and, said selected vector being a system snapshot provided for initial inclusion in a training set.
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32. A computer program product for selecting input vectors for extraction of representative data for training of an adaptive model, said computer program product comprising a computer usable medium having computer readable program code thereon, said computer readable program code comprising:
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computer readable program code means for receiving signals as input from a plurality of sensors as a set of training vectors;
computer readable program code means for ordering the set of training vectors according to a corresponding value in each vector of a particular sensor;
computer readable program code means for dividing the set of training vectors according to equally spaced ranges according to the ordering; and
computer readable program code means for selecting at least one vector from each of the equally spaced ranges for training the adaptive model. - View Dependent Claims (34, 36)
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38. A computer program product for selecting a set of training vectors representative of an adaptive system, said computer program product comprising a computer usable medium having computer readable program code thereon, said computer readable program code comprising:
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computer readable program code means for collecting historical data, said historical data including a plurality of system vectors each indicating an operating state of said system;
computer readable program code means for selecting a system parameter in said system vector space;
computer readable program code means for ordering plurality of system vectors;
computer readable program code means for binning vector space for said selected parameter; and
computer readable program code means for selecting a vector from each bin according to a selected criteria, such that selected said vectors form a training set, said training set forming said empirical model for monitoring system operation.
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Specification