Detection of Additive and Multiplicative Faults - Parity Space vs. Parameter Estimation

Höfling, Thomas; Pfeufer, Thomas

doi:10.1016/s1474-6670(17)48078-5

Cited by 31 publications

(14 citation statements)

References 8 publications

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“…A second way is to construct an observer capable of estimating the trends of some internal variables of the process [2]. Alternatively, an estimate of some process parameters can be carried out [3]. Whichever method is used, the estimated values must be compared with the actual ones in order to obtain information (called residuals) on the state of the system, and eventually detect the occurence of a fault.…”

Section: • Testing New Techniques In Fault Detection Is Very Importantmentioning

confidence: 99%

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An innovative intelligent system for sensor validation in tokamak machines

Rizzo¹,

Xibilia

2002

IEEE Trans. Contr. Syst. Technol.

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In this paper, a sensor validation strategy based on soft computing techniques to isolate and classify some faults occurring in the measurement system of a Tokamak fusion plant is described. Particular attention is focused on the system used to measure vertical stress in the mechanical structure of a Tokamak nuclear fusion plant during fusion experiments. The strategy adopted is based on a modular structure comprising two stages. The first stage consists of a neural network which acts as a symptom model able to estimate directly some suitable features of the expected sensor responses, thus allowing the most frequently occurring sensor faults to be isolated. The second stage consists of a fault classifier implemented via a fuzzy inference system, in order to exploit the knowledge of the experts. The proposed strategy has been validated at the Joint European Torus (JET), on several experiments. A comparison has been made with both traditional sensor monitoring techniques and validation performed manually by experts. A great improvement has been achieved, in terms of both fault detection and classification capabilities, and the degree of automation achieved.

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Section: • Testing New Techniques In Fault Detection Is Very Importantmentioning

confidence: 99%

“…The threshold is related to the variance of the residuals (2). Consider (3) where (4) Then, we check if (with ) during a fixed time window . Thus, if we assume that the residuals are normally distributed, with zero average, fixing means that a fault alarm is statistically raised in 5% of cases.…”

Section: Traditional Jet Measurement Monitoringmentioning

confidence: 99%

An innovative intelligent system for sensor validation in tokamak machines

Rizzo¹,

Xibilia

2002

IEEE Trans. Contr. Syst. Technol.

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“…It combines continuous-time parity equations, Hofling and Pfeufer (1994), with parameter estimation, Fig. 3.…”

Section: Adaptive Parity Equations Faultsmentioning

confidence: 99%

“…The signal M0 includes the spring pretension and external load torques. All parameters are estimated in the fault free case by recursive parameter estimation in the continuous time domain, Pfeufer (1994).…”

Section: Theoretical Modelling and Identificationmentioning

confidence: 99%

<title>Intelligent electromechanical actuators</title>

Pfeufer

Isermann

1996

SPIE Proceedings

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Servo systems play an important role in many automated processes. In order to fulfil the hard demands on reliability and fast and precise operation, intelligent concepts for the control, supervision and (re)configuration are necessary. In this paper, an approach is presented which integrates different levels of signal processing in an electromechanical servo system. The digital controller and the model-based fault detection scheme are designed taking into account model-uncertainty and the time variant process behaviour, which is caused by temperature influences, wear, aging, etc. After a brief description of the theoretical basis an experimental application shows results for an automobile servo system which is driven by a d.c. motor.

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“…The model-based approaches use either signal models leading to spectral analysis (Neumann, 1991) and parameter estimation of ARMA models, or process models leading to parameter estimation (Gomm et al, 1993), state estimation or use of parity equations. The performances of the parity space and parameter estimation approaches for the detection of additive and multiplicative faults have been compared by Höfling and Pfeufer (1994). Methods based on a state estimation using Kalman filters or observers rely on the use of accurate models, and on numerous assumptions such as known model structure and parameters, known noise characteristics and known effects of the faults on the model.…”

Section: Introductionmentioning

confidence: 99%

On-line fault diagnosis of dynamic systems via robust parameter estimation

Bloch

Ouladsine

Thomas

1995

Control Engineering Practice

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A procedure simultaneously achieving the detection of faults, their isolation and their identification is presented. The systems considered are MISO systems represented by ARX models, the parameters of which are estimated on-line by a robust procedure. A priori knowledge of the faults which can occur is used. The faults modeled here are outliers, biases or drifts, and can act upon output, inputs or even noise. The magnitude of a fault is estimated on a moving window from the prediction error sequence by least squares. Statistical tests of the significance of the estimated parameters corresponding to the different faults are performed. An application on the strip drive in the furnace of an annealing line is finally presented.

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Detection of Additive and Multiplicative Faults - Parity Space vs. Parameter Estimation

Cited by 31 publications

References 8 publications

An innovative intelligent system for sensor validation in tokamak machines

An innovative intelligent system for sensor validation in tokamak machines

<title>Intelligent electromechanical actuators</title>

On-line fault diagnosis of dynamic systems via robust parameter estimation

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