Fault Detection and Isolation (FDI) using Linear Kalman Filter (LKF) is not sufficient for effective monitoring of nonlinear processes. Most of the chemical plants are nonlinear in nature while operating the plant in a wide range of process variables. In this study we present an approach for designing of Multi Model Adaptive Linear Kalman Filter (MMALKF) for Fault Detection and Isolation (FDI) of a nonlinear system. The uses a bank of adaptive Kalman filter, with each model based on different fault hypothesis. In this study the effectiveness of the MMALKF has been demonstrated on a spherical tank system. The proposed method is detecting and isolating the sensor and actuator soft faults which occur sequentially or simultaneously. © Maxwell Scientific Organization, 2014.

Manimozhi M

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

Saravanakumar R

Fulltext

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VIT University

Research Journal of Applied Sciences, Engineering and Technology

The objective of this paper is first to design an Adaptive Linear Kalman Filter (ALKF) to estimate nonlinear process states and to compare the performance of the ALKF with the Extended Kalman Filter (EKF). The designed ALKF is next used to detect sensor and actuator biases which may occur either sequentially or simultaneously using a Multi Model ALKF (MMALKF). Finally the Multi Model Adaptive Linear Hâ Filter (MMALHâF) is designed to increase the robustness of bias Detection in the presence of unknown noise statistics and unmodeled dynamics. The proposed estimator is demonstrated on the variable area tank process and Continuously Stirred Tank Reactor (CSTR) process to show its efficacy.

Computers & Electrical Engineering

Sensor and actuator bias estimation using multi model approach

Sensor and Actuator Fault Detection and Isolation in Nonlinear System using Multi Model Adaptive Linear Kalman Filter

Fault Detection and Diagnosis in Engineering Systems

Introduction to Fault Detection and Diagnosis

International Journal of Modelling, Identification and Control

Design and implementation of simple adaptive control schemes on simulated model of CSTR process

Many chemical processes exhibit highly nonlinear dynamic behavior when operated over a wide operating range. The fault diagnosis schemes based on a linear perturbation model often prove to be inadequate, with regard to addressing fault diagnosis problems in such systems. In this work, a novel multiple-operating-regimes-based technique is proposed for performing online fault diagnosis in nonlinear systems. A Bayesian approach is used to identify the combination of linear perturbation models in different operating regimes that best-represents the plant dynamics at the current operating point. Nonlinear versions of the generalized likelihood ratio (GLR) method that use multiple linear models for fault identification are proposed. The proposed multimodel based fault diagnosis approaches are computationally efficient and exploit the linearity of each submodel. To arrest the performance degradation caused by the occurrence of faults, the information provided by the fault diagnosis component is then used for online fault accommodation. The efficacy of the proposed diagnosis schemes is demonstrated by conducting simulation studies on two benchmark continuously stirred tank reactor (CSTR) systems and a high-purity binary distillation column system. Analysis of the simulation results reveals that the proposed multimodel Kalman filter-based fault diagnosis schemes outperform the linear GLR method when a nonlinear process is in a transient state over a wide operating range. © 2008 American Chemical Society.

Industrial and Engineering Chemistry Research

Online fault diagnosis in nonlinear systems using the multiple operating regime approach

Control Engineering Practice

A practical multiple model adaptive strategy for multivariable model predictive control

Journal	Research Journal of Applied Sciences, Engineering and Technology
Publisher	Maxwell Scientific Publication Corp.
ISSN	2040-7459
Open Access	Yes