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Showing papers by "Ron J. Patton published in 2005"


Journal ArticleDOI
TL;DR: In this article, a neuro-fuzzy and de-coupling fault diagnosis scheme (NFDFDS) is proposed for fault detection and isolation of nonlinear dynamic systems.
Abstract: In this paper, a neuro-fuzzy and de-coupling fault diagnosis scheme (NFDFDS) is proposed for fault detection and isolation (FDI) of nonlinear dynamic systems. In this approach, powerful approximation and reasoning capabilities of neuro-fuzzy models are combined with the de-coupling capabilities of optimal observers to perform reliable fault detection and isolation. The neuro-fuzzy model presented here is a special form of Takagi–Sugeno (TS) fuzzy model used to represent the system by a fuzzy fusion of local linear sub-models. The necessary condition for the application of this FDI scheme is that this special form of the TS model can represent the nonlinear system, which is true for many practical systems. It is shown that if all the local models are stable and the corresponding local observers converge to the local models it can be expected that the global model is stable and the corresponding global observer will converge to the nonlinear input–output system. An application of FDI for an electro-pneumatic valve actuator in a sugar factory is presented. Key issues of finding a suitable structure for detecting and isolating nine realistic actuator faults are described. Copyright © 2004 John Wiley & Sons, Ltd.

43 citations


Journal ArticleDOI
TL;DR: Observability analysis and observer synthesis are studied for a three-tank water process and the observer design takes into account singularity of nonlinear observers.
Abstract: Observability analysis and observer synthesis are studied for a three-tank water process. Observability of the process is considered under various assumptions on measurements. The observer design takes into account singularity of nonlinear observers. The simulation studies and real data validation show satisfactory convergence of the designed observers.

25 citations


Journal ArticleDOI
TL;DR: This paper focuses attention on a recent complexity reduction method, termed Higher Order Singular Value Decomposition (HOSVD)-based complexity reduction, and Takagi-Sugeno (TS) inference operator-based fuzzy rule-bases.
Abstract: One direction of measured data-set based modeling applies fuzzy logic identification tools and results in a fuzzy rule-base model. A typical problem of fuzzy identification methods is that the complexity of the resulting fuzzy rule-base, namely the number of rules in the rule-base, explodes with the modeling accuracy. As a result, the topic of fuzzy rule-base complexity reduction techniques emerged in the last decade. A common disadvantage of fuzzy rule-base complexity reduction methods is that the resulting complexity minimized fuzzy-rule bases cannot be simply adapted to new information. If we have new information that cannot be described by the fuzzy rules of the complexity minimized fuzzy rule-base, then we have two choices. The first choice is to add new fuzzy rules to the fuzzy rule-base until the new information can be described. The second choice is to modify the new information until it can be described by the fuzzy rule-base without using additional fuzzy rules. This second case has the prominent role if the number of fuzzy rules in the fuzzy rule-base is limited. This paper proposes a method for the second choice. The proposed method minimizes the necessary modification of the new information. This paper focuses attention on a recent complexity reduction method, termed Higher Order Singular Value Decomposition (HOSVD)-based complexity reduction, and Takagi-Sugeno (TS) inference operator-based fuzzy rule-bases. An example is used to provide the validation of the proposed method. In order to demonstrate the effectiveness of the proposed method, a control system of a differential-steered automatic guided vehicle is modeled in the paper.

16 citations


Book ChapterDOI
01 Jan 2005
TL;DR: In this paper, the authors have outlined some of the recent research on FDI and fault diagnosis for dynamic systems, using this integrated approach and showed that by using evolutionary computing, neural networks and NF modelling structures realistic solutions are achievable.
Abstract: In this chapter, selected aspects of the fault diagnosis problem for control systems have been considered with special attention paid to fault detection and, to a lesser extent, fault isolation. In particular, the robustness problem of FDI with respect to the requirement to maximise sensitivity to faults while minimising (or decoupling) the effect of uncertain effects through unknown inputs has been described. This severe robustness challenge accompanying analytical methods of FDI has led to the requirement of developing new methodologies for FDI in which analytical and CI techniques are combined to achieve good global and nonlinear modelling and robustness. The chapter has outlined some of the recent research on FDI and fault diagnosis for dynamic systems, using this integrated approach. The examples have shown that by using evolutionary computing, neural networks and NF modelling structures realistic solutions are achievable. It is hoped that this direction of research will stimulate an increased adoption of real industrial application to make mode-based FDI more usual and effective in real process systems.

10 citations


Journal ArticleDOI
TL;DR: The main objective is to show how to employ the bounded error approach to solve such a challenging task that occurs in many practical situations and can be easily applied in robust fault detection schemes.

8 citations


Proceedings ArticleDOI
Ron J. Patton1
16 Jun 2005
TL;DR: In this paper, the authors present a benchmark scheme based on an intelligent electro-pneumatic valve actuator with in-situ (in-loop) testing and fault signalling in a sugar factory evaporization process.
Abstract: There have been several proposals and suggestions of benchmark studies for evaluating the performance of fault detection and isolation (FDI) methods allied to real industrial plant. The main aim of these benchmarks is to provide a training facility for the engineering community (both industry and academia) to gain an understanding and "feel" for the way in which various FDI methods can perform in a realistic control engineering application setting. This is deemed an essential step in the process of transferring the technology (often gained in the academic community) into real application. This presentation would provide the description and application of a benchmark scheme based on an intelligent electro-pneumatic valve actuator with in-situ (in the loop) testing and fault signalling in a sugar factory evaporisation process. The overall application would also be outlined and this involves on-line FDI and monitoring of the sensors and several actuators of a sugar juice evaporisation plant, providing overall monitoring of the plant under closed-loop control. The study was conducted within the Research Training Network "Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems " DAMADICS {www.eng.hull.ac.uk/research/control}, funded by the European Commission in the Human Improvement Programme of Framework 5. The FDI benchmark is method-independent and based on an in-depth study of the phenomena that can lead to likely faults in valve actuator systems. The work to be presented uses a detailed consideration of the physical and electro-mechanical properties (and their modelling requirements) of an intelligent industrial actuator. The presentation would also include the typical engineering requirements of an actuator valve operating under challenging process conditions, together with the setting up of suitable performance indices for evaluating the FDI results. The results to be described correspond to real in the loop testing and FDI evaluation with injected fault signals.

2 citations


Journal ArticleDOI
TL;DR: In this paper, a new formulation of the computation of the disturbance and fault distribution matrices is suggested for Neuro-Fuzzy and De-coupling Fault Diagnosis Scheme (NFDFDS).