Model-plant mismatch detection using model parameter data clustering fortransverse process behavior monitoring

Abstract

A method includes obtaining (402) operating data associated with operation of a cross-directional industrial process controlled by at least one model-based process controller (106, 204). The method also includes, during a training period (502a, 502b), performing (406) closed-loop model identification with a first portion of the operating data to identify multiple sets of first spatial and temporalmodels. The method further includes identifying (408) clusters (604) associated with parameter values of the first spatial and temporal models. The method also includes, during a testing period (504a, 504b), performing (410) closed-loop model identification with a second portion of the operating data to identify second spatial and temporal models. The method further includes determining (412) whether at least one parameter value of at least one of the second spatial and temporal models falls outside at least one of the clusters. In addition, the method includes, in response to such a determination (414), detecting that a mismatch exists between actual and modeled behaviors of the industrial process.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY CLAIM [0002] This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 62 / 395,950, filed September 16, 2016, which is hereby incorporated by reference in its entirety. technical field [0003] The present disclosure generally relates to measurement and control systems. More specifically, the present disclosure relates to model plant mismatch detection using support vector machines for lateral process behavior monitoring. Background technique [0004] Industrial process control and automation systems are often used to automate large and complex industrial processes. These types of control and automation systems routinely include process controllers and field devices such as sensors and actuators. Some of the process controllers typically receive measurements from sensors and generate control signals for actuators. A model-based industrial process controller is one type o...

AI Technical Summary

Problems solved by technology

However, it is often difficult to use routine operating data to determine whether the model accurately describes real process behavior

Furthermore, it is generally not advisable to perform some kind of experimentation for this purpose to improve the quality of the data, as this may interfere with the quality of the product resulting from the process

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