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Actuator fault diagnosis: an adaptive observer-based technique

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TLDR
A novel approach for the fault diagnosis of actuators in known deterministic dynamic systems by using an adaptive observer technique under the assumption that the system state observer can be designed such that the observation error is strictly positive real (SPR).
Abstract
This paper presents a novel approach for the fault diagnosis of actuators in known deterministic dynamic systems by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. Under the assumption that the system state observer can be designed such that the observation error is strictly positive real (SPR), an adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for the general system to improve robustness. The method is demonstrated through its application to a simulated second-order system.

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Citations
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Book

Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools

TL;DR: This book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers.
Journal ArticleDOI

Fault Accommodation for Nonlinear Dynamic Systems

TL;DR: This note investigates process fault accommodation in a class of nonlinear continuous-time systems using a new fault estimation module, based on an adaptive estimator, to compensate for the effect of the faults by stabilizing the closed-loop system.
Journal ArticleDOI

Adaptive state feedback and tracking control of systems with actuator failures

TL;DR: Simulation results show that desired system performance is achieved with the developed adaptive actuator failure compensation control designs.
Journal ArticleDOI

A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems

TL;DR: The analysis of the proposed fault isolation scheme provides rigorous analytical results concerning the fault isolation time, and two simulation examples are given to show the effectiveness of the fault diagnosis methodology.
Journal ArticleDOI

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

TL;DR: An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are presented to reveal the future development direction in this field.
References
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Book

Adaptive Control

TL;DR: Benefiting from the feedback of users who are familiar with the first edition, the material has been reorganized and rewritten, giving a more balanced and teachable presentation of fundamentals and applications.
Book

Stable Adaptive Systems

TL;DR: Stability theory simple adaptive systems adaptive observers the control problem persistent excitation error models robust adaptive controlThe control problem - relaxation of assumptions multivariable adaptive systems applications of adaptive control.
Book

Feedback Systems: Input-output Properties

TL;DR: In this paper, the Bellman-Gronwall Lemma has been applied to the small gain theorem in the context of linear systems and convolutional neural networks, and it has been shown that it can be applied to linear systems.
Journal ArticleDOI

Process fault detection based on modeling and estimation methods-A survey

Rolf Isermann
- 01 Jul 1984 - 
TL;DR: This contribution presents a brief summary of some basic fault detection methods, followed by a description of suitable parameter estimation methods for continuous-time models.
Journal ArticleDOI

Analytical redundancy and the design of robust failure detection systems

TL;DR: In this article, a robust failure detection and identification (FDI) process is viewed as consisting of two stages: residual generation and decision making, and it is argued that a robust FDI system can be achieved by designing a robust residual generation process.
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