Patent No. | 10,557,840 |
---|---|

Issue Date | February 11, 2020 |

Title | System And Method For Performing Industrial Processes Across Facilities |

Inventorship | Richard B. Jones, Georgetown, TX (US) |

Assignee | HARTFORD STEAM BOILER INSPECTION and INSURANCE COMPANY, Hartford, CT (US) |

1. 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.

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.