Surveillance system and method having parameter estimation and operating mode partitioning

A system for determining asset status, said system comprising in combination:

• a training data set partitionable into a plurality of training data subsets having a plurality of predefined operating modes associated therewith such that each partitioned training data subset includes at least one of said predefined operating modes associated thereto;
• means for acquiring a set of observed signal data values from an asset;
• means for determining an operating mode of the asset for the set of observed signal data values;
• means for selecting a process submodel from a process model as a function of said determined operating mode of the asset;
• means for calculating a set of estimated signal data values from said selected process submodel for said determined operating mode for performing asset surveillance, and
• means for determining asset status as a function of the calculated set of estimated signal data values.

An asset surveillance method, the steps including:

• acquiring a set of signals from an asset correlative to asset status;
• digitizing the set of signals for defining a set of digitized signals;
• creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto;
• determining an operating mode of the asset;
• selecting at least one of the process submodels as a function of the determined operating mode;
• producing a set of estimated signal values from the selected process submodel;
• detecting a presence of a fault as a function of the set of estimated signal values from the selected process submodel for providing asset surveillance.

An asset surveillance system, said system comprising in combination:

• a data acquisition means for acquiring a set of signals from an asset correlative to asset status;
• a digitizing means for digitizing said set of signals for defining a set of digitized signals;
• a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and wherein each of said partitioned training data subsets and each of said plurality of process submodels has at least one defined operating mode associated therewith;
• an operating mode determination means for determining an operating mode of said asset;
• a process model selection means for selecting at least one of said process submodels as a function of said determined operating mode;
• a parameter estimation means for producing a set of estimated signal values from said selected process submodel;
• a fault detection means for detecting the presence of a fault as a function of said set of estimated signal values from said selected process submodel for providing asset surveillance.

A method for performing surveillance of an asset, the steps including:

• creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto;
• acquiring a set of observed signal data values from the asset;
• determining an operating mode of the asset for the set of observed signal data values;
• selecting a process submodel from the process model as a function of the determined operating mode of the asset;
• calculating a set of estimated signal data values from the selected process submodel for the determined operating mode, and
• determining asset status by performing the step of comparing the set of observed signal data values to the calculated set of estimated signal data values for performing asset surveillance.

A method for performing signal source surveillance, the steps including:

• creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto;
• acquiring a set of observed signal data values from at least one signal source;
• determining an operating mode of at least the one signal source for the set of observed signal data values;
• selecting a process submodel from the process model as a function of the determined operating mode of at least the one signal source;
• calculating a set of estimated signal data values from the selected process submodel for the determined operating mode, and
• determining status of at least the one signal source as a function of the calculated set of estimated signal data values for performing signal source surveillance.

A method for performing asset control, the steps including:

• creating a process model comprised of a plurality of process submodels each correlative to at least one training data subset partitioned from an unpartitioned training data set and each having an operating mode associated thereto;
• acquiring a set of observed signal data values from the asset;
• determining an operating mode of the asset for the set of observed signal data values;
• selecting a process submodel from the process model as a function of the determined operating mode of the asset;
• calculating a set of estimated signal data values from the selected process submodel for the determined operating mode, and
• employing at least one of the calculated set of estimated signal data values as at least one virtual signal value for performing asset control.