US Pat. No. 9,111,212

DYNAMIC OUTLIER BIAS REDUCTION SYSTEM AND METHOD

HARTFORD STEAM BOILER INS...

1. A system for reducing outlier bias in target variables measured for a facility, comprising:
an input unit for inputting one or more data sets to be processed, wherein the input unit comprises a measuring device configured
to:

measure one or more target variables for the facility; and
provide a corresponding data set for each of the target variables;
a computing unit coupled to the input unit and for processing the data sets, wherein the computing unit comprises a processor
and a storage subsystem; and

an output unit coupled to the computing unit and for outputting one or more of the processed data sets received from the computing
unit,

wherein a computer program stored by the storage subsystem comprises instructions that, when executed reduces outlier bias
for one of the processed data sets by causing the processor to:

select one of the target variables for the reduction of outlier bias;
obtain a complete data set of the one of the target variables from the input unit, wherein the complete data set of the one
of the target variables comprises a plurality of inputted data values;

obtain a bias criteria used to determine one or more outliers;
determine a set of model coefficients for a mathematical model;
(1) apply the mathematical model with the set of model coefficients to the complete data set to determine a set of model predicted
values;

(2) generate an error set by comparing the set of model predicted values to corresponding actual values of the complete data
set;

(3) generate a set of error threshold values from the error set and the bias criteria;
(4) generate a removed data set comprising elements of the complete data set with corresponding error set values outside the
set of error threshold values;

(5) generate a censored data set comprising all elements of the complete data set that are not within the removed data set;
(6) determine a set of updated model coefficients for the mathematical model based on the censored data set; and
(7) repeat steps (1)-(6) as an iteration unless a censoring performance termination criteria is satisfied, whereby at the
iteration the set of predicted values, the error set, the set of error threshold values, the removed data set, and the censored
data set are generated using the set of updated model coefficients.

US Pat. No. 10,409,891

FUTURE RELIABILITY PREDICTION BASED ON SYSTEM OPERATIONAL AND PERFORMANCE DATA MODELLING

HARTFORD STEAM BOILER INS...

1. A system, comprising:at least one measurable system that comprises a plurality of equipment assets that is operated at each respective facility of a plurality of facilities;
at least one measuring device;
wherein the at least one measuring device measures, to generate measuring data, one or more physical attribute, one or more characteristics, or both that are associated with an operation, a performance, or both, of the measurable system of each respective facility of the plurality of facilities;
at least one sensing device;
wherein the at least one sensing device senses, to generate sensing data, the one or more physical attribute, the one or more characteristics, or both that are associated with the operation, the performance, or both, of the measurable system of each respective facility of the plurality of facilities;
a processor that is operationally coupled to:
i) the at least one the at least one measuring device, the at least one sensing device, or both, and
ii) a non-transitory computer readable medium,
wherein the non-transitory computer readable medium comprises instructions which, when executed by the processor, cause the processor to:
receive maintenance expense data of the at least one measurable system for each respective facility of the plurality of facilities;
receive first principle data that comprises, for one or more first principle characteristics associated with one or more target variables of the at least one measurable system, the measuring data, the sensing data, or both;
receive asset reliability data of the at least one measurable system;
receive one or more comparative analysis models associated with the at least one measurable system;
utilize one or more comparative analysis models to generate at least one maintenance standard for the at least one measureable system, based on the maintenance expense data and the first principle data;
generate a plurality of category values that categorizes, by at least one designated interval, the maintenance expense data based upon the at least the one maintenance standard associated with the at least one measureable system;
determine an estimated future reliability data of the at least one measurable system based on the asset reliability data and the plurality of category values;
wherein the one or more comparative analysis models identifies one or more reliability-effective maintenance tasks that affect the one or more target variables of the at least one measurable system based at least in part on at least one primary first principle characteristic;
wherein the at least one primary first principle characteristic is determined based on an amount of the variation in the one or more target variables of the at least one measurable system between the plurality of facilities;
wherein, based on performance of the one or more reliability-effective maintenance tasks with the at least one measurable system, the at least one measuring device, the at least one sensing device, or both, obtain, intermittently or continuously, current data for the at least one primary first principle characteristic of the at least one measurable system and transmit the current data to the processor that updates the estimated future reliability data of the at least one measurable system to generate the updated estimated future reliability data of the at least one measurable system; and
an user interface configured to display the estimated future reliability data and the updated estimated future reliability data.

US Pat. No. 10,557,840

SYSTEM AND METHOD FOR PERFORMING INDUSTRIAL PROCESSES ACROSS FACILITIES

HARTFORD STEAM BOILER INS...

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.