System and method to assess signal similarity with applications to diagnostics and prognostics
First Claim
1. A method for assessing dynamic system similarity, said method comprising the following steps:
- providing at least one multivariate test signal associated with an operational system;
providing at least one multivariate reference signal associated with the operational system;
comparing the signal similarity of the second-order moment of the at least one multivariate test signal with the second-order moment of the at least one multivariate reference signal;
establishing a selected threshold level defining a maximum allowable deviation for the signal similarity of the second-order moments of the at least one multivariate test and reference signals;
determining when the signal similarity of the second-order moments of the at least one multivariate test and reference signals fails to remain within the selected threshold level; and
providing output to a user indicating instances when the selected threshold level is exceeded;
wherein said step of comparing the signal similarity is performed using time-domain processing techniques and more particularly comprises the steps of;
calculating the auto-covariance matrix functions for the at least one respective multivariate test and reference signals;
calculating the covariance of the auto-covariance of the at least one multivariate reference signal;
generating a test statistic using in part the calculated auto-covariance matrix functions for the at least one respective multivariate test and reference signals and the covariance of the auto-covariance of the at least one multivariate reference signal;
determining when the test statistic crosses the selected threshold level.
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Abstract
Signal processing technology for assessing dynamic system similarity for fault detection and other applications is based on time- and frequency-domain time series analysis techniques and compares the entire autocorrelation structure of a test and reference signal series. The test and reference signals are first subjected to similar pre-processing to help guarantee signal stationarity. Pre-processing may include formation of multivariate signal clusters, filtering and sampling. Multivariate periodograms or autocovariance functions are then calculated for each signal series. Test statistics are computed and assessed to determine the equality of the test and reference signals. When the difference between sample autocovariance functions or periodograms of such signals exceeds a preselected threshold value, fault detection signals and/or related diagnostic information are provided as output to a user.
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Citations
15 Claims
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1. A method for assessing dynamic system similarity, said method comprising the following steps:
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providing at least one multivariate test signal associated with an operational system; providing at least one multivariate reference signal associated with the operational system; comparing the signal similarity of the second-order moment of the at least one multivariate test signal with the second-order moment of the at least one multivariate reference signal; establishing a selected threshold level defining a maximum allowable deviation for the signal similarity of the second-order moments of the at least one multivariate test and reference signals; determining when the signal similarity of the second-order moments of the at least one multivariate test and reference signals fails to remain within the selected threshold level; and providing output to a user indicating instances when the selected threshold level is exceeded; wherein said step of comparing the signal similarity is performed using time-domain processing techniques and more particularly comprises the steps of; calculating the auto-covariance matrix functions for the at least one respective multivariate test and reference signals; calculating the covariance of the auto-covariance of the at least one multivariate reference signal; generating a test statistic using in part the calculated auto-covariance matrix functions for the at least one respective multivariate test and reference signals and the covariance of the auto-covariance of the at least one multivariate reference signal; determining when the test statistic crosses the selected threshold level. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A system for assessing dynamic system similarity, comprising:
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a signal processor adapted to; (i) receive at least one multivariate test signal and at least one multivariate reference signal associated with an operational system; (ii) compare the signal similarity of the second-order moment of the at least one multivariate test signal with the second-order moment of the at least one multivariate reference signal; and (iii) determine when the signal similarity of the second-order moments of the at least one multivariate test and reference signals fails to remain within a preselected threshold of signal similarity; an output device coupled to said signal processor, said output device configured to provide visual or audio output to a user indicating instances when the selected threshold level of signal similarity is not met; and wherein said signal processor is further adapted to; calculate the auto-covariance matrix functions for the at least one respective multivariate test and reference signals; calculate the covariance of the auto-covariance of the at least one multivariate reference signal; generate a test statistic using in part the calculated auto-covariance matrix functions for the at least one respective multivariate test and reference signals and the covariance of the auto-covariance of the at least one multivariate reference signal; and determine when the test statistic fails to remain within the preselected threshold of signal similarity. - View Dependent Claims (9, 10, 11, 12, 13, 14, 15)
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Specification