Robust Observer Design with Application to Fault Detection

Viswanadham, N.; Minto, K.D.

doi:10.23919/acc.1988.4789939

Cited by 34 publications

(5 citation statements)

References 12 publications

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“…Much attention has been focused on improving the robustness of analytical redundancy methods to model uncertainty. Two of the more popular methods in the literature include robust residual generators [88,108,322], and structured residuals with an unknown input observer [91,248,286]. The simplest approach is to model the uncertainties as additional disturbances.…”

Section: Detection Properties Of the Residualmentioning

confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,160

811

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An alternative group of methods has emerged which do not require the use of an explicit model. This is the key basic construct for the data-driven paradigm. Model-free and non-parametric methods for fault detection, process optimisation and control design are currently at a particularly exciting stage of development.This new advanced textbook by Chiang, Russell and Braatz primarily tackles the data-driven routes to Fault Detection and Diagnosis. It is an outgrowth of a prior Advances in Industrial Control monograph; Russell, Chiang and Braatz. Data-driven Techniques for Fault Detection and Diagnosis in ChemicalProcesses, 2000, ISBN 1-85233-258-1. The new textbook expands the material of the monograph and gives a fuller presentation of some of the alternative modelbased methods, the analytical methods, and of the knowledge-based techniques. vi Series Editors' ForewordThis allows the reader to compare and contrast the different approaches to the problem of fault detection and diagnosis. Thus the text is suitable for advanced courses for process, chemical and control engineers.

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Section: Detection Properties Of the Residualmentioning

confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,160

811

View full text Add to dashboard Cite

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“…The partial state g is defined as [ = D(z)-'u,y = N(z) [ (1980). An observer that exactly reconstructs the partial state is given from the solution of the Diophantine equation X(z) N(z) + Y(z) D(z) = I (Vidyasagar 1985, Viswanadham andMinto 1988). The observer configuration is shown in Fig.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The difference between the estimate of the plant's output and the real (measured) output was used as the residual for fault detection. Viswanadham and Minto (1988) were among the first to propose an H , optimization technique to design a robust observer for residual generation. Ding er al.…”

Section: Introductionmentioning

confidence: 99%

Residual generation and fault detection for discrete-time systems using an l∞ technique

Faitakis

Kantor

1996

International Journal of Control

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The paper presents a new design for a robust output observer and demonstrates that it can be used to detect sensor faults in uncertain systems with output disturbances. The time-domain design uses an I, technique to minimize a norm of the estimation error. The estimation error forms a residual signal that is the basis for fault detection, fault isolation and disturbance rejection. The minimum detectable fault and the maximum non-detectable fault are introduced to quantify the sensitivity of the detection scheme at a given alarm threshold. These quantities are obtained as solutions to non-convex optimization problems.

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“…The post-filter Q(s) has a full degree of freedom that can be used for further design specifications. Viswanadham and Minto (1988) have solved the problem of optimal robust observer design by minimizing the 2-norm with the aid of H 00 optimization. Thus, they select the partial state that is not corrupted by unknown inputs and use it for robust estimation.…”

Section: Res) = Q(s)(nj(s)f(s) + N D(s)d(s))mentioning

confidence: 99%