Trending system
First Claim
1. A trending system comprising:
- a sliding window filter; and
wherein;
the sliding window filter receives a data set from a clock system;
the data set comprises a plurality of data points;
the sliding window filter selects multiple data windows in the data set;
each of the data windows includes a subset plurality of the data points in the data set;
the sliding window filter generates upper confidence bounds and lower confidence bounds for each data point through regression and interval estimation over each of the multiple data windows that includes the data point;
the sliding window filter compares the interval widths of the confidence intervals produced by regression over each of the multiple data windows that includes the data point;
the sliding window filter selects a fit for each data point that results in the smallest confidence interval for that data point; and
the sliding window filter generates a filtered estimate of the data set from the selected upper confidence bounds and lower confidence bounds for each data point; and
a trend change detection mechanism, wherein;
the trend change detection mechanism determines a first convex hull for a set of upper confidence bounds and a second convex hull for a set of lower confidence bounds;
a comparator mechanism compares the first convex hull and the second convex hull to determine a transition point in the data set; and
an estimated trend of the data set is determined from the transition point and data after the transition point.
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Abstract
A trending system and method for trending data in a physical or clock system. The trending system includes a sliding window filter. The sliding window filter receives a data set of data points generated by the clock system. The sliding window filter partitions the data set into a plurality of data windows, and uses the data windows to calculate upper and lower confidence bounds for the data set. Specifically, the sliding window filter calculates upper confidence bounds and lower confidence bounds for each data point using each of the multiple data windows that includes the data point. The sliding window filter then selects the upper confidence bounds and the lower confidence bounds that result in the smallest mean prediction confidence interval for that data point. This results in a smoothed estimated trend for the data set that can be used for prognostication and fault detection.
282 Citations
21 Claims
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1. A trending system comprising:
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a sliding window filter; and wherein; the sliding window filter receives a data set from a clock system; the data set comprises a plurality of data points; the sliding window filter selects multiple data windows in the data set; each of the data windows includes a subset plurality of the data points in the data set; the sliding window filter generates upper confidence bounds and lower confidence bounds for each data point through regression and interval estimation over each of the multiple data windows that includes the data point; the sliding window filter compares the interval widths of the confidence intervals produced by regression over each of the multiple data windows that includes the data point; the sliding window filter selects a fit for each data point that results in the smallest confidence interval for that data point; and the sliding window filter generates a filtered estimate of the data set from the selected upper confidence bounds and lower confidence bounds for each data point; and a trend change detection mechanism, wherein; the trend change detection mechanism determines a first convex hull for a set of upper confidence bounds and a second convex hull for a set of lower confidence bounds; a comparator mechanism compares the first convex hull and the second convex hull to determine a transition point in the data set; and an estimated trend of the data set is determined from the transition point and data after the transition point. - View Dependent Claims (2, 3, 4)
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5. A trending system comprising:
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a sliding window filter; and wherein; the sliding window filter receives a data set from a clock system; the data set comprises a plurality of data points; the sliding window filter selects multiple data windows in the data set; each of the data windows includes a subset plurality of the data points in the data set; the sliding window filter generates upper confidence bounds and lower confidence bounds for each data point through regression and interval estimation over each of the multiple data windows that includes the data point; the sliding window filter compares the interval widths of the confidence intervals produced by regression over each of the multiple data windows that includes the data point; the sliding window filter selects a fit for each data point that results in the smallest confidence interval for that data point; the sliding window filter generates a filtered estimate of the data set from the selected upper confidence bounds and lower confidence bounds for each data point; an outlier elimination mechanism; and wherein; the outlier elimination mechanism removes statistical outliers in the data set by generating a first prediction cone for data points in a left sample window, generating a second prediction cone for data points in a right sample window, and determining if data points in a test window reside in the first prediction cone or the second prediction cone; and a data point in the test window that does not reside in the first prediction cone or the second prediction cone is an outlier. - View Dependent Claims (6, 7, 8)
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9. A trending system comprising:
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a trending program; and computer readable medium bearing the trending program; and wherein; the trending program comprises a sliding window filter; the sliding window filter receives a data set from a physical system; the data set comprises a plurality of data points; the sliding window filter selects multiple data windows in the data set; each of the data windows has a subset plurality of the data points in the data set; the sliding window filter generates upper confidence bounds and lower confidence bounds for each data point using each of the multiple data windows that includes the data point; the sliding window filter selects an upper confidence bound and a lower confidence bound for each data point that results in the smallest confidence interval for that data point; wherein, the trending program further comprises an outlier elimination mechanism; the outlier elimination mechanism removes statistical outliers in the data set by generating a first prediction cone for data points in a left sample window, generating a second prediction cone for data points in a right sample window, and determining if data points in a test window reside in the first prediction cone or the second prediction cone; and a data point in the test window that does not reside in the first prediction cone or the second prediction cone is an outlier. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification