Model-Based Fault Detection and Diagnosis - Status and Applications

Isermann, Rolf

doi:10.1016/s1474-6670(17)32149-3

Cited by 570 publications

(355 citation statements)

References 10 publications

Supporting

Mentioning

348

Contrasting

Unclassified

Order By: Relevance

“…where Q ik is defined in (15). If B 0 has full row rank, (16) and (17) are removed from the conditions; if B 1 has full row rank, (18) and (19) are removed.…”

Section: Lemma 4 For the Basic Case Of An Ftcs Inmentioning

confidence: 99%

“…Fault effects on system dynamics are described by different system matrices in the dynamic model (1)-(2), A(ζ (t)), B(ζ (t)), C(ζ (t)), and D(ζ (t)) depending on ζ(t). The FDI scheme can be designed by standard model-based methods using these dynamic models [15]. Although some iterative algorithms exist to obtain a sequence estimate of Markov states based on the probabilistic description of system modes, the computational cost is not suitable for online implementation and controller reconfiguration, and the algorithms are designed for a discrete-time Markov chain only [6].…”

Section: Synthesis Of Generator Matricesmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic Design of Fault Tolerant Control via Parameterization

Zhao

2007

Circuits Syst Signal Process

View full text Add to dashboard Cite

This paper studies the design of fault tolerant control systems (FTCSs) by considering random faults and two categories of design objectives. The FTCSs are modeled in a stochastic framework, resulting from the random fault process. The design objectives include a stability requirement and a probabilistic performance index. Such an index is chosen as a reliability-related criterion, which can be evaluated from a numerical procedure only but lacks analytical expressions. A parameterization procedure together with a randomization-based optimization method is developed to find a statistically optimal controller that can stabilize the system and achieve the highest reliability.

show abstract

“…where Q ik is defined in (15). If B 0 has full row rank, (16) and (17) are removed from the conditions; if B 1 has full row rank, (18) and (19) are removed.…”

Section: Lemma 4 For the Basic Case Of An Ftcs Inmentioning

confidence: 99%

Section: Synthesis Of Generator Matricesmentioning

confidence: 99%

Probabilistic Design of Fault Tolerant Control via Parameterization

Zhao

2007

Circuits Syst Signal Process

View full text Add to dashboard Cite

show abstract

“…The problem of fault isolation can be reduced to a number of fault detection problems using banks of observers. The idea dates back to Wünnenberg and Frank [6]; subsequent developments are summarized in [1,7,8]. The observers generate residuals, which can be interpreted as fault indicators.…”

Section: Introductionmentioning

confidence: 99%

“…This principle is extended in [11][12][13] by fault isolation observers (FIOs). These observers allow to isolate faults using only a single, specifically parametrized observer reducing both design and implementation effort compared with classical bank-of-observers approaches [1,[6][7][8]. Different design methods have been proposed for FIOs.…”

Section: Introductionmentioning

confidence: 99%

Design of robust fault isolation observers for linear systems with bounded uncertain parameters

Wahrburg¹

2015

Adaptive Control & Signal

View full text Add to dashboard Cite

In this article, a design procedure for robust fault isolation observers for linear systems is proposed. It allows to detect and isolate faults by means of a single, specifically parametrized observer, yielding a small implementation effort compared with standard bank-of-observers approaches. A design based on parametric eigenstructure assignment for nominal system dynamics is presented. The main contribution is to show how the remaining degrees of freedom can be utilized to optimize robustness of the fault isolation observer with respect to uncertain system parameters. Therein, the uncertainties are assumed to belong to a polyhedral domain. The application of the design to two examples verifies the results for systems defined by nominal dynamics and known bounds on the uncertain parameters.

show abstract

“…The complexity means that the probability of fault occurrence can be significant and an automatic supervisory control system should be used to detect and isolate anomalous working conditions as early as possible. These issues pushed a great attention on fault detection and isolation (FDI) in dynamic processes the last two decades and a wide variety of so-called model-based approaches have been proposed to tackle this problem [1,2]. Model-based methods all use mathematical models of the plant being monitored, either in state space or transfer function forms.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of a simulated industrial gas turbine via identification approach

Simani

2006

Adaptive Control & Signal

View full text Add to dashboard Cite

In this paper, a model-based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors-in-variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single-shaft industrial gas turbine MATLAB/ SIMULINK 1 simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte-Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI.

show abstract

Model-Based Fault Detection and Diagnosis - Status and Applications

Cited by 570 publications

References 10 publications

Probabilistic Design of Fault Tolerant Control via Parameterization

Probabilistic Design of Fault Tolerant Control via Parameterization

Design of robust fault isolation observers for linear systems with bounded uncertain parameters

Fault diagnosis of a simulated industrial gas turbine via identification approach

Contact Info

Product

Resources

About