Change detection using directional statistics
AssigneeInternational Business Machines Corporation
InventorsTsuyoshi Ide, Jayant R. Kalagnanam, Dung T. Phan
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Abstract
A method includes capturing multivariate time-series data comprising two or more data sets from a system captured over a past time period and a present time period, applying at least two sliding time windows to the multivariate time-series data in determining respective data matrices, computing an orthonormal matrix for each of the data matrices, wherein the orthonormal matrix is a signature of fluctuation patterns of a respective data matrix, computing a difference between at least two of the data sets in the past and the present time periods through the orthonormal matrices, and detecting a fault in at least one of the systems by comparing the difference to a threshold.
Claims16
A method comprising:
- capturing multivariate time-series data comprising two or more data sets from a system captured over a past time period and a present time period;
- applying at least two sliding time windows to the multivariate time-series data in determining respective data matrices;
- computing an orthonormal matrix for each of the data matrices, wherein the orthonormal matrix is a signature of fluctuation patterns of a respective data matrix;
- computing a difference between at least two of the data sets in the past and the present time periods through the orthonormal matrices;
- detecting a fault in at least one of the systems by comparing the difference to a threshold and removing the system from service upon detected the fault.
A non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform a method comprising:
- capturing multivariate time-series data comprising two or more data sets from a system captured over a past time period and a present time period;
- applying at least two sliding time windows to the multivariate time-series data in determining respective data matrices;
- computing an orthonormal matrix for each of the data matrices, wherein the orthonormal matrix is a signature of fluctuation patterns of a respective data matrix;
- computing a difference between at least two of the data sets in the past and the present time periods through the orthonormal matrices;
- detecting a fault in at least one of the systems by comparing the difference to a threshold and removing the system from service upon detected the fault.
Dependencies are inferred from the claim text.