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US 2014/0278303 A1Published application
Method and system of dynamic model identification for monitoring and control of dynamic machines with variable structure or variable operation conditions
OwnerWallace LARIMOREinventor
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Abstract
A method and system for identification of nonlinear parameter-varying systems via canonical variate analysis. Various implementations of these methods and systems may be implemented on various platforms and may include and of a variety of applications and physical implementations.
Claims9
A method of forming a dynamic model for the behavior of machines from available data, comprising:
- obtaining operating data from a machine in operation;
- fitting an autoregressive (ARX) linear parameter varying (LPV) model for at least one of orders and states of a machine based on the operating data collected from the machine in operation;
- removing effects of future inputs on future outputs from the model;
- determining a corrected future for the model;
- performing, with a processor, a weighted singular value decomposition (SVD) between an augmented past and the corrected future;
- choosing a state order;
- fitting state space (SS) equation coefficient estimates;
- fitting noise coefficient estimates;
- and
- generating a dynamic model of machine behavior.
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A method that transforms a set of measured data from a machine in operation into a dynamic model for behavior of the machine, comprising:
- a plurality of data collection devices that collect and transmit data from a machine in operation to a database;
- an optimal order generated by an autoregressive (ARX) linear parameter varying (LPV) model that computes an Akaike information criterion (AIC) for each order or data in the database;
- a processor that performs a canonical variate analysis (CVA) between the collected data in the database and corrected future data;
- a series of optimal candidate state estimates determined by the CVA;
- a processor that sorts the optimal candidate state estimates by predictive ability of the optimal candidate state estimates;
- a processor that computes the AIC for each state order to select an optimal state order and computes a state space LPV model for at least the optimal state order.
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Dependencies are inferred from the claim text.