Statistical Processing Methods used in Abnormal Situation Detection
First Claim
1. A method of detecting an abnormal situation associated with a process plant, comprising:
- receiving measured data pertaining to a process parameter sensed by at least one sensor device associated with the process plant;
determining one or more statistical measures associated with the process parameter using the measured data; and
using the one or more statistical measures associated with the process parameter to detect an abnormal situation within the process plant.
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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.
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Citations
69 Claims
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1. A method of detecting an abnormal situation associated with a process plant, comprising:
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receiving measured data pertaining to a process parameter sensed by at least one sensor device associated with the process plant; determining one or more statistical measures associated with the process parameter using the measured data; and using the one or more statistical measures associated with the process parameter to detect an abnormal situation within the process plant. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38)
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39. A method of detecting an abnormal situation in a fluid catalytic cracker, comprising:
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receiving measurements of a process parameter in the fluid catalytic cracker; determining a statistical measure of the process parameter from the process parameter measurements; comparing the statistical measure of the process parameter to a baseline value; and detecting the existence of an abnormal situation based on the comparison of the statistical measure of the process parameter to the baseline value. - View Dependent Claims (40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50)
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51. A method of detecting an abnormal situation in a distillation column, comprising:
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receiving measurements of a process parameter in the distillation column; determining a statistical measure of the process parameter from the process parameter measurements; comparing the statistical measure of the process parameter to a baseline value; and detecting the existence of an abnormal situation based on the comparison of the statistical measure of the process parameter to the baseline value. - View Dependent Claims (52, 53, 54, 55, 56)
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57. A method of processing data collected in a process plant, comprising:
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using a first set of the collected data points to determine a block length for calculating one or more statistical measures of the collected data including; determining a frequency component of the first set of the collected data points, determining a dominant system time constant from the frequency component; and setting the block length based on the dominant system time constant; and using the block length to determine a number of data points to use in calculating the one or more statistical measures of the collected data. - View Dependent Claims (58, 59, 60, 61)
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62. A method of fitting a sine wave to data collected within a process plant, comprising:
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determining 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 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 the variable transformation to produce a set of transformed data points from the data collected within the process plant; performing a linear regression to fit the transformed data points to the linear expression; and determining the second set of sine wave parameters based on the linear regression. - View Dependent Claims (63, 64, 65, 66, 67, 68, 69)
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Specification