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US 10,557,840 B2Granted
System and method for performing industrial processes across facilities
AssigneeHartford Steam Boiler Inspection and Insurance Company
InventorRichard B. Jones
Life of this patent
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Abstract
A system and method is described herein for performing at least one industrial process at each facility of a plurality of facilities based on an industrial process standard generated by reducing functional, and trend line outlier bias in data of one or more process parameters as measured by one or more sensors. Outliers are removed from the data set through an objective method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line estimates. Outlier data records are removed when the error values are greater than or equal to one or more criteria.
Claims22
A system, comprising:
- a plurality of facilities;
- wherein each facility of the plurality of facilities performs at least one industrial process;
- at least one sensor configured to:
- i) measure, based on a model for the at least one industrial process, one or more target variables of at least one process parameter of the at least one industrial process being performed at a particular facility of the plurality of facilities during a particular time;
- and
- ii) generate a corresponding current process-related data set for each respective target variable;
- a computing unit coupled to the at least one measuring sensor, wherein the computing unit comprises at least one processor and a non-transient storage subsystem;
- a computer program that is stored by the non-transient storage subsystem, wherein the computer program, when executed by the at least one processor, causes the at least one processor of the computing unit to at least:
- receive the corresponding current process-related data set for each respective target variable;
- generate a random process data set from the corresponding current process-related data set;
- obtain a set of bias criteria values used to determine one or more process-related outliers;
- perform a dynamic outlier bias reduction on the corresponding current process-related data set for one or more bias criteria values of the set of bias criteria values to generate one or more outlier bias reduced, process-related target data set;
- perform the dynamic outlier bias reduction on the process-related random data set for the one or more bias criteria values of the set of bias criteria values to generate one or more outlier bias reduced, process-related random data set;
- calculate a set of target error values for the one or more outlier bias reduced, process-related target data set and a set of random error values for the one or more outlier bias reduced, process-related random data set;
- calculate a set of target correlation coefficients for the one or more outlier bias reduced, process-related target data sets and a set of random correlation coefficients for the one or more outlier bias reduced, process-related random data set;
- generate a first bias criteria curve for the corresponding current process-related data set and a second bias criteria curve for the process-related random data set from the one or more bias criteria values, the set of target error values, the set of random error values, the set of target correlation coefficients, and the set of random correlation coefficients;
- dynamically determine, based on the first bias criteria curve and the second bias criteria curve, a non-biased viability of the corresponding current process-related data set;
- wherein the non-biased viability is an indicator of whether the corresponding current process-related target data set is representative of the at least one process parameter of the at least one industrial process being modeled by the model;
- and
- dynamically generate, when the non-biased viability identifies that the corresponding current process-related target data set is representative of the at least one process parameter of the at least one industrial process, at least one industrial process standard;
- and
- wherein each facility of the plurality of facilities continues to perform the least one industrial process based at least in part on the at least one industrial process standard.
19 more elements
A method, comprising:
- receiving by a computer unit, a current process-related data set for each respective target variable of at least one process parameter of the at least one industrial process being performed at a particular facility of a plurality of facilities during a particular time;
- wherein the current process-related data set has been generated by at least one measuring sensor at the particular facility, wherein the at least one measuring sensor is configured to:
- i) measure, based on a model for the at least one industrial process, one or more target variables;
- and
- ii) generate the current process-related data set for each respective target variable;
- generating, by the computer unit, a random process data set from the current process-related data set;
- obtaining, by the computer unit, a set of bias criteria values used to determine one or more process-related outliers;
- performing, by the computer unit, a dynamic outlier bias reduction on the current process-related data set for one or more bias criteria values of the set of bias criteria values to generate one or more outlier bias reduced, process-related target data set;
- performing, by the computer unit, the dynamic outlier bias reduction on the process-related random data set for the one or more bias criteria values of the set of bias criteria values to generate one or more outlier bias reduced, process-related random data set;
- calculating, by the computer unit, a set of target error values for the one or more outlier bias reduced, process-related target data set and a set of random error values for the one or more outlier bias reduced, process-related random data set;
- calculating, by the computer unit, a set of target correlation coefficients for the one or more outlier bias reduced, process-related target data sets and a set of random correlation coefficients for the one or more outlier bias reduced, process-related random data set;
- generating, by the computer unit, a first bias criteria curve for the current process-related data set and a second bias criteria curve for the process-related random data set from the one or more bias criteria values, the set of target error values, the set of random error values, the set of target correlation coefficients, and the set of random correlation coefficients;
- dynamically determining, by the computer unit, based on the first bias criteria curve and the second bias criteria curve, a non-biased viability of the current process-related data set;
- wherein the non-biased viability is an indicator of whether the current process-related target data set is representative of the at least one process parameter of the at least one industrial process being modeled by the model;
- and
- dynamically generating, by the computer unit, when the non-biased viability identifies that the current process-related target data set is representative of the at least one process parameter of the at least one industrial process, at least one industrial process standard;
- and
- continuing to perform the at least one industrial process at each facility of the plurality of facilities based at least in part on the at least one industrial process standard.
15 more elements
Dependencies are inferred from the claim text.