On the uncertainty identification for linear dynamic systems using stochastic embedding approach with gaussian mixture models

Rafael Orellana, Rodrigo Carvajal, Pedro Escárate, Juan C. Agüero

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In control and monitoring of manufacturing processes, it is key to understand model uncertainty in order to achieve the required levels of consistency, quality, and economy, among others. In aerospace applications, models need to be very precise and able to describe the entire dynamics of an aircraft. In addition, the complexity of modern real systems has turned deterministic models impractical, since they cannot adequately represent the behavior of disturbances in sensors and actuators, and tool and machine wear, to name a few. Thus, it is necessary to deal with model uncertainties in the dynamics of the plant by incorporating a stochastic behavior. These uncertainties could also affect the effectiveness of fault diagnosis methodologies used to increment the safety and reliability in real-world systems. Determining suitable dynamic system models of real processes is essential to obtain effective process control strategies and accurate fault detection and diagnosis methodologies that deliver good performance. In this paper, a maximum likelihood estimation algorithm for the uncertainty modeling in linear dynamic systems is developed utilizing a stochastic embedding approach. In this approach, system uncertainties are accounted for as a stochastic error term in a transfer function. In this paper, we model the error-model probability density function as a finite Gaussian mixture model. For the estimation of the nominal model and the probability density function of the parameters of the error-model, we develop an iterative algorithm based on the Expectation-Maximization algorithm using the data from independent experiments. The benefits of our proposal are illustrated via numerical simulations.

Original languageEnglish
Article number3837
JournalSensors
Volume21
Issue number11
DOIs
StatePublished - 1 Jun 2021
Externally publishedYes

Keywords

  • Expectation-maximization
  • Gaussian mixture model
  • Maximum likelihood
  • Stochastic embedding
  • Uncertainty modeling

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