Method and system for analyzing and predicting the behavior of systems
First Claim
1. A method for analyzing and predicting the behavior of a system, comprising the steps of:
- continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the time-based baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and
determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial.
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Abstract
A monitoring system including a baseline model that automatically captures and models normal system behavior, a correlation model that employs multivariate autoregression analysis to detect abnormal system behavior, and an alarm service that weights and scores a variety of alerts to determine an alarm status and implement appropriate response actions. The baseline model decomposes the input variables into a number of components representing relatively predictable behaviors so that the erratic component e(t) may be isolated for further processing. These components include a global trend component, a cyclical component, and a seasonal component. Modeling and continually updating these components separately permits a more accurate identification of the erratic component of the input variable, which typically reflects abnormal patterns when they occur.
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Citations
43 Claims
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1. A method for analyzing and predicting the behavior of a system, comprising the steps of:
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continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the time-based baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and
determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method for analyzing and predicting the behavior of a system, comprising the steps of:
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continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the baseline mean for the selected input variable;
computing a forecast estimate for the selected input variable based on erratic components computed for other input variables and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and
determining an alert status for the forecast estimate. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27)
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28. A method for analyzing and predicting the behavior of a system, comprising the steps of:
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continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables;
determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial;
computing a forecast estimate for the selected input variable based on erratic components computed for other input variables and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and
determining an alert status for the forecast estimate. - View Dependent Claims (29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)
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Specification