Statistical processing methods used in abnormal situation detection
First Claim
1. A computer-implemented method of fitting a sine wave to data collected within a process plant, comprising:
- determining, using a processor, a first set of parameters of the sine wave based on one or more statistical measures of the process parameter determined from the data collected within the process plant;
storing, within a memory, a variable transformation of a mathematical expression of the sine wave that produces a linear expression having a second set of sine wave parameters associated therewith;
using, via a processor, the variable transformation to produce a set of transformed data points from the data collected within the process plant;
performing, using a processor, a linear regression to fit the transformed data points to the linear expression; and
determining, using a processor, the second set of sine wave parameters based on the linear regression.
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Abstract
Detection of one or more abnormal situations is performed using various statistical measures, such as a mean, a median, a standard deviation, etc. of one or more process parameters or variable measurements made by statistical process monitoring blocks within a plant. This detection is enhanced in various cases by using specialized data filters and data processing techniques, which are designed to be computationally simple and therefore are able to be applied to data collected at a high sampling rate in a field device having limited processing power. The enhanced data or measurements may be used to provided better or more accurate statistical measures of the data, may be used to trim the data to remove outliers from this data, may be used to fit this data to non-linear functions, or may be use to quickly detect the occurrence of various abnormal situations within specific plant equipment, such as distillation columns and fluid catalytic crackers.
26 Citations
8 Claims
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1. A computer-implemented method of fitting a sine wave to data collected within a process plant, comprising:
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determining, using a processor, a first set of parameters of the sine wave based on one or more statistical measures of the process parameter determined from the data collected within the process plant; storing, within a memory, a variable transformation of a mathematical expression of the sine wave that produces a linear expression having a second set of sine wave parameters associated therewith; using, via a processor, the variable transformation to produce a set of transformed data points from the data collected within the process plant; performing, using a processor, a linear regression to fit the transformed data points to the linear expression; and determining, using a processor, the second set of sine wave parameters based on the linear regression. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification