Nonlinear fault tolerant control based parameter estimation diagnosis: Application to induction motors

Chouiref, Houda; Boussaid, Boumedyen; Abdelkrim, Mohamed Naceur; Aubrun, Christophe

doi:10.1109/ssd.2013.6564118

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Several identification algorithms have been utilized in a context to the IM's stator or rotor windings faults diagnosis. They have based on: last square error minimization [20], Kalman Filter [22], [22], [23], [24], [25], [26], [27], nonlinear programming [21], state observers [23], [28], [29], Moving Horizon Estimation, [30], [31], genetic algorithm [31], Sparse grid optimization [32], trustregion and Broyden-Fletcher-Goldfard-Shano (TR-BFGS) technique [30], and Sliding Mode Observer [34].…”

Section: Introductionmentioning

confidence: 99%

Induction motor windings faults detection using flux-error based MRAS estimators

Bednarz¹,

Dybkowski²

2019

Diagnostyka

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The paper is concerned with detection of a stator and rotor winding faults in a squirrel-cage induction motor. The idea of the fault detection is based on a hypothesis that each of windings faults results in a sharp increase or decrease of internal parameters' values of the machine, therefore it can be treated as a suitable fault symptom. Resistances of the stator and rotor windings seem to be adequate quantities due to their direct relationship with the machine windings. An observation and analysis of the parameters' changes in a realtime domain enables to an incipient detection of the fault. It is evident that internal parameters of the machine can't be measured directly during operation on the drive system thus the only way is an estimation by specialized algorithms. In the paper two estimators based on Model Reference Adaptive System (MRAS) were utilized to achieve this goal. Two simple algorithms for faults detection are proposed as well. Detailed description of fault detection systems is included in the paper. Proposed systems were tested on computer simulations performed by MATLAB/Simulink software. Then, experimental tests were carried out on the laboratory setup to confirm usefulness of proposed approaches.

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Section: Introductionmentioning

confidence: 99%

Induction motor windings faults detection using flux-error based MRAS estimators

Bednarz¹,

Dybkowski²

2019

Diagnostyka

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“…Fault diagnosis consists of comparing the behavior of the real system with that of its model. In an ideal case, the system and the model behave exactly the same and when a fault is detected the behaviors are different, this difference is termed as residual, this difference between real system and model behaviors, can be used to diagnose and isolate the malfunction [5].…”

Section: Introductionmentioning

confidence: 99%

An LPV modelling and fault diagnosis in wind turbine benchmark system

Aubrun

Puig²,

Abdelkrim³

et al. 2017

IJMIC

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In this paper, we present a fault detection approach of a linear parameter varying (LPV) system using residuals generator based on the use of the predictor based LPV subspace identification approach (LPV PBSID). To address this problem of fault detection in the pitch subsystem considered in the wind turbine benchmark introduced in IFAC SAFEPROCESS 2009, the failure events are caused by jumps in the damping ratio and natural frequency values of the model. The damping ratio is increased and the natural frequency is decreased with the hydraulic pressure variation. Satisfactory results have been obtained of the proposed approach using the proposed fault scenarios.

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Robust nonlinear filter for nonlinear systems with multiplicative noise uncertainties, unknown external disturbances, and packet dropouts

Zhang

Geng

et al. 2017

Intl J Robust & Nonlinear

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Summary This study is concerned with the robust nonlinear filtering problem for nonlinear discrete‐time stochastic system with multiplicative noise uncertainties, unknown external disturbances, and packet dropouts. The focus of this paper is to design a filter with predictor–corrector structure such that the upper bound on the state estimation error variance is minimized in the presence of multiplicative noise, unknown external disturbances, and packet dropouts. Thus, a robust nonlinear filter based on the method to obtain the upper bound on variances of multiplicative noises, unknown disturbances, and packet dropouts is designed. Further stability analysis shows that the proposed filter has robustness against multiplicative noises, unknown external disturbances, and packet dropouts. Simulation results show that the proposed filter is more effective than extended Kalman filter and other robust extended Kalman filter. Copyright © 2017 John Wiley & Sons, Ltd.

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Nonlinear fault tolerant control based parameter estimation diagnosis: Application to induction motors

Cited by 8 publications

References 15 publications

Induction motor windings faults detection using flux-error based MRAS estimators

Induction motor windings faults detection using flux-error based MRAS estimators

An LPV modelling and fault diagnosis in wind turbine benchmark system

Robust nonlinear filter for nonlinear systems with multiplicative noise uncertainties, unknown external disturbances, and packet dropouts

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