Performance model configuration for asset performance management

A non-transitory computer readable medium storing instructions executable by a processing resource that when executed, cause the processing resource to:

• extract, by a machine learning model, a plurality of coordinates from a plurality of data points comprising a performance curve associated with an operational performance of a mechanical device operating under a predefined performance parameter;
• generate, by the machine learning model, a digitized performance curve for the mechanical device from the plurality of coordinates;
• extract, by the machine learning model, a plurality of baseline coefficients from the digitized performance curve;
• transfer the extracted plurality of baseline coefficients to an asset model associated with the mechanical device;
• validate, by the asset model, the extracted plurality of baseline coefficients via image processing of the extracted plurality of baseline coefficients and supplied design data corresponding to the predefined performance parameter;
• predict, by the asset model, an operational performance curve for the mechanical device based on the extracted plurality of baseline coefficients and predefined performance parameter;
• monitor a performance of the mechanical device using the predicted operational performance curve;
• and
• adjust at least one operating condition associated with the mechanical device based on the predicted operational performance curve.

A computing device for asset performance management, comprising:

• a processing resource;
• and
• a memory resource storing non-transitory machine-readable instructions that when executed, cause the processing resource to:
• receive a plurality of performance curves associated with an operational performance of a mechanical device;
• identify, by a machine learning model of the computing device, a design case associated with each performance curve among the plurality of performance curves of the mechanical device;
• identify, by the machine learning model, an operational stage of the mechanical device based on each performance curve;
• generate, by the machine learning model, a digitized performance curve for the mechanical device based on the identified design case and the identified operational stage of the mechanical device;
• extract, by the machine learning model, a plurality of baseline coefficients from the digitized performance curve;
• transfer the extracted plurality of baseline coefficients to an asset model associated with the mechanical device;
• validate, by the asset model, the extracted plurality of baseline coefficients via image processing of the extracted plurality of baseline coefficients and supplied design data corresponding to the identified design case and the identified operational stage of the mechanical device;
• predict, by the asset model, an operational performance curve for the mechanical device based on the extracted plurality of baseline coefficients;
• and
• monitor a performance of the mechanical device using the predicted operational performance curve.