Observer-based Fault Diagnosis Algorithm Research Articles

Minimum Entropy Active Fault Tolerant Control of the Non-Gaussian Stochastic Distribution System Subjected to Mean Constraint

Stochastic distribution control (SDC) systems are a group of systems where the outputs considered is the measured probability density function (PDF) of the system output whilst subjected to a normal crisp input. The purpose of the active fault tolerant control of such systems is to use the fault estimation information and other measured information to make the output PDF still track the given distribution when the objective PDF is known. However, if the target PDF is unavailable, the PDF tracking operation will be impossible. Minimum entropy control of the system output can be considered as an alternative strategy. The mean represents the center location of the stochastic variable, and it is reasonable that the minimum entropy fault tolerant controller can be designed subjected to mean constraint. In this paper, using the rational square-root B-spline model for the shape control of the system output probability density function (PDF), a nonlinear adaptive observer based fault diagnosis algorithm is proposed to diagnose the fault. Through the controller reconfiguration, the system entropy subjected to mean restriction can still be minimized when fault occurs. An illustrative example is utilized to demonstrate the use of the minimum entropy fault tolerant control algorithms.

Fault Diagnosis of Exhaust Gas Recirculation and Variable Geometry Turbocharger Systems in a Passenger Car Diesel Engine Based on a Sliding Mode Observer for Air System States Estimation

In this paper, we propose a sliding mode observer based fault diagnosis algorithm for diesel engines with exhaust gas recirculation (EGR) and variable geometry turbocharger (VGT) systems. The nonlinear sliding mode observer is proposed for precise states estimation of air system in diesel engines. Based on the estimation results of the observer and the limited sensor information in mass-produced engines, a residual generation model is derived. A modified cumulative summation algorithm is applied to the residual generation model for robust fault detection and isolation of the EGR and VGT systems. The proposed observer based fault diagnosis algorithm is implemented on a real-time embedded system, and the bypass function of an engine management system (EMS) is applied to generate multiple types of fault conditions in the systems. As a result of this study, estimation performance of the proposed observer is validated and successful fault diagnosis of the EGR and VGT systems is demonstrated through engine experiments.

Fault diagnosis and fault tolerant control for the non-Gaussian time-delayed stochastic distribution control system

The purpose of fault diagnosis of stochastic distribution control (SDC) systems is to use the measured input and the system output probability density functions (PDFs) to obtain the fault information of the SDC system. When the target PDF is known, the purpose of fault tolerant control of stochastic distribution control system is to make the output PDF still track the given distribution using the fault tolerant controller. However, in practice, time delay may exist in the data (or image) processing, the modeling and transmission phases. When time delay is not considered, the effectiveness of the fault detection, diagnosis and fault tolerant control of stochastic distribution systems will be reduced. In this paper, the rational square-root B-spline is used to approach the output probability density function. In order to diagnose the fault in the dynamic part of such systems, it is then followed by the novel design of a nonlinear neural network observer-based fault diagnosis algorithm. The time delay term will be deleted in the stability proof of the observation error dynamic system. Based on the fault diagnosis information, a new fault tolerant controller based on PI tracking control is designed to make the post-fault probability density function still track the given distribution, which is dependent of the time delay term. Finally, simulations for the particle distribution control problem are given to show the effectiveness of the proposed approach.

Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model

The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Convergency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demonstrate the effectiveness of the proposed algorithm.

Fault detection, diagnosis and tolerant control for non-Gaussian stochastic distribution systems using a rational square-root approximation model

Stochastic Distribution Control (SDC) systems are a group of systems where the outputs considered are the measured Probability Density Functions (PDFs) of the system output whilst subjected to a normal crisp input. The purpose of the control algorithm design of such systems is to choose a control input such that the PDF of the system output can follow a prespecified PDF as close as possible. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper a non-linear adaptive observer-based fault diagnosis algorithm is proposed to diagnose the fault in the dynamic part of such systems. Using the estimation to the unknown fault, a fault tolerant control via a controller reconfiguration is proposed, where it has been shown that a good output PDF tracking can still be realised when fault occurs in the system. A simulated example is given to illustrate the use of the proposed algorithm.