System for extraction of representative data for training of adaptive process monitoring equipment
First Claim
1. A method of monitoring a system instrumented with sensors by selecting input vectors for extraction of representative data for training of an adaptive model, the input vectors being organized along an independent (‘
- x’
) dimension to enable selection of vectors along a dependent (‘
y’
) dimension, the method comprising;
receiving signals automatically as input to a computer and as a set of training vectors, said signals being derived from a plurality of the sensors and representing physical parameters of the monitored system;
automatically ordering the set of training vectors according to a corresponding value in each vector of a particular sensor and assigning each training vector a sequence number according to the ordering to form the ‘
x’
dimension of the data with the sequence numbers;
automatically dividing the set of training vectors according to equally spaced ranges selected across the magnitude of the data, the magnitude forming the ‘
y’
dimension of the data;
automatically selecting at least one vector from each of the equally spaced ranges while selecting less than all of the training vectors of the equally spaced ranges for training the adaptive model; and
automatically training the adaptive model with only the vectors selected in the selecting step and with training calculations that use sensor reading values from the signals that form the vectors.
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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
37 Claims
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1. A method of monitoring a system instrumented with sensors by selecting input vectors for extraction of representative data for training of an adaptive model, the input vectors being organized along an independent (‘
- x’
) dimension to enable selection of vectors along a dependent (‘
y’
) dimension, the method comprising;receiving signals automatically as input to a computer and as a set of training vectors, said signals being derived from a plurality of the sensors and representing physical parameters of the monitored system; automatically ordering the set of training vectors according to a corresponding value in each vector of a particular sensor and assigning each training vector a sequence number according to the ordering to form the ‘
x’
dimension of the data with the sequence numbers;automatically dividing the set of training vectors according to equally spaced ranges selected across the magnitude of the data, the magnitude forming the ‘
y’
dimension of the data;automatically selecting at least one vector from each of the equally spaced ranges while selecting less than all of the training vectors of the equally spaced ranges for training the adaptive model; and automatically training the adaptive model with only the vectors selected in the selecting step and with training calculations that use sensor reading values from the signals that form the vectors. - View Dependent Claims (2, 3, 4, 5, 6, 7)
- x’
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8. An apparatus for training an adaptive model for monitoring a system instrumented with sensors, comprising:
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data acquisition means for automatically acquiring signals from sensors representative of operational states of the monitored system; an empirical modeling module responsive to the data acquisition means for automatically providing indications about the operational states of the monitored system; a data store for automatically storing modeling parameters for use by the empirical modeling module; and a training module to automatically distill characteristic operational sensor data acquired from the monitored system to a representative set of sensor data for storing in the data store and to train an empirical model of said empirical modeling module, by selecting, from the characteristic operational sensor data, time-correlated observations representative of regularly spaced intervals, wherein the number of observations selected is less than the total number of observations within the intervals, and selected across the magnitude of the data and forming a ‘
y’
dimension of the data, and selected along an ordering of the observations according to values in the observations of a particular sensor, wherein each observation is assigned a sequence number according to the ordering, the sequence numbers forming the ‘
x’
dimension of the data. - View Dependent Claims (9, 10, 11, 12, 13)
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14. A method of monitoring a system instrumented with sensors by selecting a set of training vectors representative of a system, said training set forming an empirical model of said system, said method comprising the steps of:
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a) automatically collecting historical data by using a computer and a memory, said historical data including a plurality of system vectors each indicating an operating state of said system, each of said vectors containing elements derived from values of a plurality of sensors representing physical system parameters of said system; b) automatically selecting one of said system parameters of said system; c) automatically ordering said plurality of system vectors according to a value of each said vector related to said selected system parameter, assigning a sequence number to each said ordered vector according to the ordering, and using the sequence numbers to represent an ‘
x’
dimension of data;d) automatically binning said plurality of vectors according to said ordering of said selected parameter by forming bins selected across the magnitude of the data, wherein the magnitude forms a ‘
y’
dimension of the data;e) automatically selecting a vector from each bin; f) only selected said vectors forming a training set while including less than all ordered vectors in the training set, and said training set forming said empirical model for monitoring system operation including training with calculations that use sensor reading values from said historical data and used to form said selected vectors. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23)
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24. A system for automatically 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, each snapshot having values for a plurality of parameters associated with the monitored system and being a vector; a memory storing said system snapshots; a sorter sorting collected system snapshots responsive to a selected system parameter and sorting the system snapshots into an order based on the value of each snapshot corresponding to the selected parameter, assigning each system snapshot a sequence number according to the order, and using the sequence numbers to form an ‘
x’
dimension of data;a plurality of bins selected across the magnitude of data and forming a ‘
y’
dimension of the data; anda vector selector binning sorted snapshots as the vectors and selecting a vector from each bin and, only said selected vector being a system snapshot provided to include in a training set so that said training set includes less than all of said vectors included within the bins. - View Dependent Claims (25, 26, 27, 28)
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29. 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 readable medium having executable instructions thereon which when executed cause a processor to execute:
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a step for receiving signals as input from a plurality of sensors as a set of training vectors; a step for automatically ordering the set of training vectors according to a corresponding value in each vector of a particular sensor, assigning a sequence number to each ordered vector according to the ordering, and using the sequence numbers to form an ‘
x’
dimension of data;a step for automatically dividing the set of training vectors according to equally spaced ranges selected across the magnitude of the data and forming a ‘
y’
dimension of the data; anda step for automatically selecting at least one vector, but less than all vectors, from each of the equally spaced ranges, to train the adaptive model. - View Dependent Claims (30, 31, 32, 33, 34, 35, 36, 37)
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Specification