Robust Fault Detection for an Induction Machine

Benloucif, M. L.; Balaska, Hanane

doi:10.1109/wac.2006.375960

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Assuming linear magnetic circuits and a balanced three-phase system in the (a, b, c) frame, the electrical equations of the induction motor expressed in the two-phase stationary (d, q) reference frame (Benloucif and Balaska 2006) are:…”

Section: Model Of the Induction Motormentioning

confidence: 99%

“…Robust residual generation based on unknown input observers to achieve disturbance decoupling may provide an efficient solution to fault detection and isolation problems. As far as linear systems are concerned the problem of robust residual generation may be considered to be mature ( (Benloucif and Staroswiecki 2002, Frank 1990and Patton and Chen 1997 whereas the FDI problem for nonlinear dynamic systems has been investigated to a lesser extent (Benloucif and Balaska 2006;Garcia andFrank 1997 andJiang et al 2001).…”

Section: Introductionmentioning

confidence: 99%

“…In (Benloucif and Mehennaoui 2002) a fault diagnosis procedure for linear systems used a combination of an analytical residual generator based on Kalman filtering and a fuzzy neural network for residual evaluation. In this work, an extension of the neuro-fuzzy FDI scheme given in (Benloucif and Balaska 2006) is proposed. It is based on a two step neural network procedure: The first network which has the ability to model a wide class of nonlinear dynamic systems acts as an on-line residual generator.…”

Section: Introductionmentioning

confidence: 99%

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Neuro-Fuzzy Sensor Fault Diagnosis of an Induction Motor

Benloucif

2011

Jou. Eng. Res.

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In this paper, a neuro-fuzzy fault diagnosis scheme is presented and its ability to detect and isolate sensor faults in an induction motor is assessed. This fault detection and isolation (FDI) approach relies on a combination of neural modelling and fuzzy logic techniques which can deal effectively with nonlinear dynamics and uncertainties. It is based on a two step neural network procedure: a first neural network is used for residual generation and a second fuzzy neural network performs residual evaluation. Simulation results are given to demonstrate the efficiency of this FDI approach.

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Section: Model Of the Induction Motormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neuro-Fuzzy Sensor Fault Diagnosis of an Induction Motor

Benloucif

2011

Jou. Eng. Res.

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“…Res. Essays Benloucif et al (2006) presented a scheme of observers with nonlinear decoupling for residual generation to detect the faults in the induction machine. Sarah (2008) presented a sliding mode observer with unique properties.…”

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confidence: 99%

Fault tolerant control of induction motor through observer techniques II

Gaeid¹

2012

Sci. Res. Essays

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In the design of the control law due to sensor and actuator faults, it is very important to implement the fault tolerant control system. Unknown Input Observers (UIO) can be used in model based fault detection and isolation (FDI) schemes to reduce or almost eliminate the effect of unknown disturbances on the MIMO (Multiple-Input and Multiple-Output) plant/system. Furthermore, they can be used to generate residuals that are insensitive to unknown disturbances or noise, and thus, lower the false alarm rate. Design of many faults isolation banks of UIOs has been done. To make sure the best detection and isolation of the faults is provided, these banks generate residuals which are sensitive to only one fault. Fault tolerant control is used to compensate both sensor and actuator faults.

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