State estimation of a nonlinear system using particle filter

Anandhakumar, K.; Ali, Ihsan; Selvakumar, K.; Raja, K B

doi:10.1109/iccic.2014.7238313

Cited by 1 publication

(1 citation statement)

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“…Although particle filter algorithms are widely used in robotics and electronics [4,5], their utilization in chemical processes is far less. The method has been used for single continuous stirred tank reactors (CSTRs) a couple of times [6,7], mainly for the purposes of process control [8,9], but rarely applied in cascade reactors and fault diagnostics. A recently developed version of the algorithm is intelligent particle filters (IPFs) [10], but they have yet to be applied for the state estimation of reactors.…”

Section: Introductionmentioning

confidence: 99%

Goal-Oriented Tuning of Particle Filters for the Fault Diagnostics of Process Systems

Kenyeres

Abonyi

2023

Processes

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This study introduces particle filtering (PF) for the tracking and fault diagnostics of complex process systems. In process systems, model equations are often nonlinear and environmental noise is non-Gaussian. We propose a method for state estimation and fault detection in a wastewater treatment system. The contributions of the paper are the following: (1) A method is suggested for sensor placement based on the state estimation performance; (2) based on the sensitivity analysis of the particle filter parameters, a tuning method is proposed; (3) a case study is presented to compare the performances of the classical PF and intelligent particle filtering (IPF) algorithms; (4) for fault diagnostics purposes, bias and impact sensor faults were examined; moreover, the efficiency of fault detection was evaluated. The results verify that particle filtering is applicable and highly efficient for tracking and fault diagnostics tasks in process systems.

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