Online estimation of the internal states is a perquisite for monitoring, control, and fault
diagnosis of many engineering processes. A cost effective approach to monitor these …

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with
Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear …

Fault diagnosis plays a vital role in ensuring safe and efficient operation of modern process
plants. Despite the encouraging progress in its research, developing a reliable and …

This work deals with state estimation in the presence of delayed and infrequent
measurements. While most measurements (referred to as secondary measurements) are …

This paper investigates constrained Bayesian state estimation problems by using a Particle
Filter (PF) approach. Constrained systems with nonlinear model and non-Gaussian …

Ensemble Kalman methods constitute an increasingly important tool in both state and
parameter estimation problems. Their popularity stems from the derivative-free nature of the …

The quality of process data in a chemical plant significantly affects the performance and
benefits gained from activities like performance monitoring, online optimization and control …

Recent results in the development of efficient large-scale nonlinear programming (NLP)
algorithms have led to fast, on-line realizations of optimization-based methods for nonlinear …

This paper addresses the state-estimation problem for nonlinear systems for the case in
which prior knowledge is available in the form of interval constraints on the states …

This work presents a constrained abridged Gaussian sum extended Kalman filter
(constrained AGS–EKF) that employs Gaussian mixture models to improve the estimation of …