Abstract: A system and method is described herein for data filtering to reduce functional, and trend line outlier bias. Outliers are removed from the data set through an objective statistical method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line calculations. Outlier data records are removed when the error values are greater than or equal to the user-supplied criteria. For optimization methods or other iterative calculations, the removed data are re-applied each iteration to the model computing new results. Using model values for the complete dataset, new error values are computed and the outlier bias reduction procedure is re-applied. Overall error is minimized for model coefficients and outlier removed data in an iterative fashion until user defined error improvement limits are reached. The filtered data may be used for validation, outlier bias reduction and data quality operations.

Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity

Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity

Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity

Abstract: A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.

Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity

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.

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.

Abstract: Systems, methods, and apparatuses for improving future reliability prediction of a measurable system by receiving operational and performance data, such as maintenance expense data, first principle data, and asset reliability data via an input interface associated with the measurable system. A plurality of category values may be generated that categorizes the maintenance expense data by a designated interval using a maintenance standard that is generated from one or more comparative analysis models associated with the measureable system. The estimated future reliability of the measurable system is determined based on the asset reliability data and the plurality of category values and the results of the future reliability are displayed on an output interface.

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.

Abstract: A system and method is described herein for data filtering to reduce functional, and trend line outlier bias. Outliers are removed from the data set through an objective statistical method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line calculations. Outlier data records are removed when the error values are greater than or equal to the user-supplied criteria. For optimization methods or other iterative calculations, the removed data are re-applied each iteration to the model computing new results. Using model values for the complete dataset, new error values are computed and the outlier bias reduction procedure is re-applied. Overall error is minimized for model coefficients and outlier removed data in an iterative fashion until user defined error improvement limits are reached. The filtered data may be used for validation, outlier bias reduction and data quality operations.

Abstract: A system and method is described herein for data filtering to reduce functional, and trend line outlier bias. Outliers are removed from the data set through an objective statistical method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line calculations. Outlier data records are removed when the error values are greater than or equal to the user-supplied criteria. For optimization methods or other iterative calculations, the removed data are re-applied each iteration to the model computing new results. Using model values for the complete dataset, new error values are computed and the outlier bias reduction procedure is re-applied. Overall error is minimized for model coefficients and outlier removed data in an iterative fashion until user defined error improvement limits are reached. The filtered data may be used for validation, outlier bias reduction and data quality operations.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to a system and method for insuring a minimum return on investment for an insured.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to systems and products for providing a computer implemented insurance product.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to a system and method for establishing a rating system to determine the impact on an insured's credit risk.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to a method for pricing an insurance policy for insuring a minimum level of return on investment.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to systems and products for providing a computer implemented insurance product.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. Various embodiments disclosed herein relate to systems and methods for determining optimal risk acceptance values associated with implementing an economic improvement plan for a facility.

Abstract: In the present invention, an insurance product, rating system and method generally relates to a rating and pricing system for quantifying the risk that the annual savings will not fall below specified levels associated with implementing and maintaining economic improvements. The product, system and method can be applied to various industries, including, power generation, petro-chemical, manufacturing and refining facilities. The present invention comprises an insurance product rating and pricing system designed for a relatively small number of insured annually or over a multi-year term with each insured having a relatively large exposure. These savings will produce additional benefits to the client in the form of enhanced creditworthiness and resulting increased availability of financing and reduced cost of financing.

Abstract: In a method for ultrasonically measuring the thickness between opposed surfaces of a workpiece from one surface, a velocity parameter of ultrasonic longitudinal waves through the material of a workpiece is measured at a location along a first surface of the workpiece. Ultrasonic longitudinal waves are transmitted from one surface to the opposite surface, and the ultrasonic waves reflected from the opposite surface are received and a time parameter between transmission of the longitudinal waves and reception of the reflected waves is measured. The thickness between the opposed surfaces is determined from the measured velocity parameter and the measured time parameter. A device for measuring the thickness of a workpiece from one surface includes a transducer assembly with a pair of spaced apart transducer units each having a coupling member and a piezoelectric element mounted thereon.