Abstract: A method includes identifying one of multiple regions in a range where an output (OP) value used to implement a manipulated variable is located. The manipulated variable is associated with an industrial process, and the OP value represents an output of a downstream controller. The method also includes calculating an achievable manipulated variable (MV) limit for the manipulated variable based on the region in which the OP value is located. For example, when the OP value is located in one region, the achievable MV limit could match a user-specified limit or be based on a gain between the OP value and a value of a process variable. When the OP value is located in another region, the achievable MV limit could track the value of the process variable with a gap.

Abstract: A method includes obtaining historical data associated with an industrial process, which is associated with multiple independent variables. The method also includes automatically excluding at least one portion of the historical data and automatically extracting data segments from at least one non-excluded portion of the historical data. The method further includes iteratively performing model identification using the data segments to identify one or more models and using the model(s) to design, monitor, or tune at least one industrial process controller for the industrial process. Iteratively performing the model identification includes recursively analyzing the data segments to (i) select the data segment(s) associated with each variable that have a highest energy and provide a high signal to noise ratio and (ii) eliminate poorly performing segments associated with each variable.

Abstract: One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: One method includes obtaining a preliminary model associated with a system to be controlled and constructing a weighted model using one or more weighting factors. The method also includes identifying a final model of the system using the preliminary and weighted models, where the final model has a stability margin that is greater than an uncertainty associated with the final model. The method further includes controlling the system using a controller designed based on the final model. Another method includes identifying a first model associated with a system to be controlled, performing model order reduction to identify a second model, and controlling the system using a controller designed based on the second model. Performing the model order reduction includes reducing a weighted coprime factor model uncertainty between the first and second models.

Abstract: A method includes identifying a signal and a disturbance associated with a model. The signal and disturbance are identified using historical data associated with one or more process variables. The method also includes decomposing the signal and the disturbance at a plurality of resolution levels. The method further includes extracting a plurality of data segments from the signal using the decomposed signal and the decomposed disturbance. In addition, the method includes determining a quality of the model using the extracted data segments and at least a portion of the historical data.

Abstract: An apparatus, method, and computer program are provided for automated closed-loop identification of an industrial process in a process control system. Multiple models (such as multiple model structure-model order combinations) can be identified, where the models are associated with a process to be controlled. One or more metrics (such as a prediction metric or rank) can be determined for each of the models. At least one of the models can be selected based on the one or more metrics. A final model for controlling the process can be provided (such as to a controller), where the final model is based on the at least one selected model. A band pass filter could be designed using some of the identified models. The band pass filter could be used to identify at least one other of the models or to determine at least one of the one or more metrics.

Abstract: A method, apparatus, and computer program are provided for identifying possible indicators of a defective valve. The method, apparatus, and computer program decompose a signal having a plurality of process variable measurements into a plurality of resolution levels. The process variable measurements are associated with operation of a valve. The method, apparatus, and computer program group the resolution levels into a plurality of groups. The method, apparatus, and computer program identify one or more defect indicators for at least some of the resolution levels using the groups. The one or more defect indicators are associated with a possible defect in the valve.

Abstract: A method, apparatus, and computer program are provided for identifying a defective valve. The method, apparatus, and computer program identify one or more indicators of a possible defect in the valve at a plurality of resolution levels. The one or more indicators are identified using at least one of one or more operating characteristics associated with the valve. The method, apparatus, and computer program generate a plurality of indexes associated with the resolution levels, where the indexes are based on the one or more indicators and identify a likelihood of a valve defect. The method, apparatus, and computer program select one of the plurality of resolution levels using at least one of the indexes. The method, apparatus, and computer program determine an overall probability of a valve defect using at least one index associated with the selected resolution level.

Abstract: A method includes identifying a signal and a disturbance using historical data associated with one or more process variables. The method also includes decomposing the signal and the disturbance at a plurality of resolution levels. The method further includes identifying a plurality of points associated with the decomposed signal using the decomposed signal and the decomposed disturbance. In addition, the method includes selecting at least some of the points and extracting a plurality of data segments from the signal, where the data segments are associated with the selected points.