Honeywell International Inc.

Rajni Jain

Ranganathan Srinivasan

Petr Endel

Sanjay Kantilal Dave

Sriram Hallihole

Mandar Subhash Vartak

Madhukar Madhavamurthy Gundappa

Gobinath Pandurangan

Lucy Ning Liu

Ward MacArthur

Christopher Chapman

S Chandrakanth Vittal

Avijit Das

Sreesathya Sathyabhama Sreekantan

Rajeev Naduthota

Roshan Yohannan

A controller and loop performance monitoring system is coupled to a controller, detects loop performance degradation in time, and diagnoses a cause of the loop performance degradation. If the cause of loop performance degradation is poor controller tuning, a re-tuning mechanism is triggered. If the cause of loop performance degradation is external to the controller (a disturbance acting on the loop, hardware malfunction etc.), an action defined in control strategy is taken, or the user is informed via alarm, user interface, or upper layer software that collects the performance measures. The monitoring itself is designed to be recursive and with low memory demands, so it can be implemented directly in the controller, without need for data transfer and storage. The monitoring is modular, consisting of oscillation detection and diagnosis part, performance indices part, internal logic part, and triggering part, easily extensible by other performance indices or parts (e.g. for overshoot monitoring). The oscillation detection and diagnosis part includes controller output oscillation monitoring, the performance indices part includes predictability index and offset index. The outputs of the controller and loop performance monitoring are overall loop performance together with loop diagnosis information, and overall controller performance together with controller diagnosis. The outputs of the controller and loop performance monitoring are used as parts of controller and loop performance monitoring user interface.

Controller and loop performance monitoring in a heating, ventilating, and air conditioning system

A method includes, using at least one processing device, obtaining multiple diagnostic indicators associated with at least a portion of an industrial process system and combining the diagnostic indicators to form a generalized indicator. Each diagnostic indicator has a value, and the generalized indicator is associated with a position on a continuous scale. The continuous scale could include a color gradient, and the method could include displaying the generalized indicator along the color gradient with a color based on its position. Multiple generalized indicators associated with multiple portions of the process system could be displayed within a torus or circle, and different portions of the torus or circle can be associated with different portions of the process system. Different concentric tori or circles could be associated with different periods of time, and at least one concentric torus or circle could identify a predicted behavior of the process system.

Apparatus and method for providing a generalized continuous performance indicator

A method of dynamic model selection for hybrid linear/non-linear process control includes developing a plurality of process models including at least one linear process model and at least one non-linear process model from inputs including dynamic process data from a processing system that runs a physical process. At least two of the plurality of process models are selected based on a performance comparison based on at least one metric, wherein the selected process models number less than a number of the plurality of process models received. A multi-model controller is generated that includes the selected process models. The physical process is simulated using the multi-model controller by applying the selected process models to obtain closed loop performance test data for each of the selected models. The performance test data is compared. A selected process model is then selected.

Dynamic model generation for implementing hybrid linear/non-linear controller

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Rajni Jain