Signal differentiation system using improved non-linear operator
First Claim
1. A tool for system modeling and monitoring a system with a plurality of sensors, each sensor generating a signal representative of a system parameter, said tool comprising:
- a memory storing a plurality of historical snapshots of one or more sensor signals, said plurality of snapshots forming a training matrix ({overscore (D)}) corresponding to a universe of identified states of a monitored system;
a data acquisition unit receiving signals from said sensors, each received signal being representative of a system parameter at a selected time;
an information processor coupled to said data acquisition unit acquiring real-time snapshots as state vectors ({right arrow over (Y)}input) indicative of observed states of said monitored system;
a similarity operator ({circle over (x)}SSCOP) implemented within said information processor operable on state vectors with said training matrix from said memory to determine similarity as a function of the absolute difference between like sensor values divided by expected sensor ranges; and
said information processor generating an expected state vector ({right arrow over (Y)}expected) responsive to said similarity operator.
1 Assignment
0 Petitions
Accused Products
Abstract
A system for detecting subtle differences in a signal in a set of linearly and/or non-linearly related signals that characterize a sensor-instrumented machine, process or living system. The system employs an improved similarity operator for signal differentiation. Signals or data representative of several linearly and/or non-linearly related parameters that describe a machine, process or living system are input to the inventive system, which compares the input to acceptable modeled states. If one or more of the input signals or data are different than expected, given the relationships between the parameters, the inventive system will indicate that difference. The system can provide expected parameter values, as well as the differences between expected and input signals; or the system can provide raw measures of similarity between the collection of input signals and the collection of acceptable modeled states. The system can be embodied in software or in a micro-controller.
-
Citations
49 Claims
-
1. A tool for system modeling and monitoring a system with a plurality of sensors, each sensor generating a signal representative of a system parameter, said tool comprising:
-
a memory storing a plurality of historical snapshots of one or more sensor signals, said plurality of snapshots forming a training matrix ({overscore (D)}) corresponding to a universe of identified states of a monitored system;
a data acquisition unit receiving signals from said sensors, each received signal being representative of a system parameter at a selected time;
an information processor coupled to said data acquisition unit acquiring real-time snapshots as state vectors ({right arrow over (Y)}input) indicative of observed states of said monitored system;
a similarity operator ({circle over (x)}SSCOP) implemented within said information processor operable on state vectors with said training matrix from said memory to determine similarity as a function of the absolute difference between like sensor values divided by expected sensor ranges; and
said information processor generating an expected state vector ({right arrow over (Y)}expected) responsive to said similarity operator. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A method of empirically monitoring a system comprising:
-
a) building a training set matrix ({overscore (D)}) of historical system snapshots, said training set matrix describing a universe of identified states of a system being monitored;
b) receiving a state vector representative of an actual state of said monitored system, said state vector including a plurality of time related sensor parameter values;
c) comparing said received state vector ({right arrow over (Y)}input) against vectors from said training set matrix to provide measures of similarity to said received state vector and states in said training set matrix based on the absolute difference of corresponding sensor values, normalized by the expected range of each sensor; and
d) generating an estimated state vector ({right arrow over (Y)}expected) from results of the comparison step (c). - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
-
-
21. A computer program product for empirically monitoring a system, said computer program product comprising a computer usable medium having computer readable program code thereon, said computer readable program code comprising:
-
computer readable program code means for receiving state vectors representative of actual states of a system being monitored, each said state vector including a plurality of time related sensor parameter values;
computer readable program code means for building a training set matrix ({overscore (D)}) of historical system snapshots, said training set matrix describing a universe of acceptable states of said monitored system;
computer readable program code means for comparing said received state vectors ({right arrow over (Y)}input) against vectors from said training set matrix;
computer readable program code means for generating expected state vectors ({right arrow over (Y)}expected) from results of said comparison; and
computer readable program code means for generating an alarm signal indicative of a difference between the operational state and normal states of operation of the monitored system, based on estimates. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41, 42, 43)
-
-
29. An apparatus for monitoring an operating condition of a selected system, comprising:
-
a first data source for providing reference data characteristic of an operating condition of a reference system;
a second data source for providing selected data characteristic of an operating condition of said selected system;
a similarity module operative to determine at least one measure of similarity of said selected data for said selected system relative to said reference data of said reference system, by dividing the absolute difference of related data points from said selected data and said reference data, by an expected range of the related data points in said reference data, and subtracting from one.
-
-
36. A similarity engine, comprising:
-
a memory for storing a plurality of known state vectors;
an input bus for providing a current state vector; and
a processor disposed to render a measure of similarity between the current state vector from said input bus and a selected known state vector from said memory, equal to a statistical combination of a set of similarity values for corresponding elements of the current state vector and the selected known state vector, where a similarity value for a comparison of an element from the current state vector to a corresponding element from the selected known state vector is a function of a quantity theta, theta being the absolute difference of said corresponding elements divided by the range of values for corresponding elements across the plurality of known state vectors.
-
-
44. A method for determining a measure of similarity between a current sate of a system and a previously known state of the system comprising the steps of:
-
acquiring sensor values from a set of sensors indicative of said current state;
for each sensor in said set;
determining an expected range over prior known values of the sensor, determining the absolute value of the difference of the current state sensor value and the sensor value from the previously known state, and calculating a similarity value for the sensor between its current state value and its previously known state value as a function of the result of said absolute value divided by said range; and
statistically combining the similarity values for the set of sensors to provide a measure of similarity between the current state and the previously known state. - View Dependent Claims (45, 46, 47, 48, 49)
-
Specification