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A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor

  • Imadeddine Harzelli
  • Arezki Menacer
  • Tarek Ameid
Original Research
  • 73 Downloads

Abstract

This paper presents a contribution to the fault monitoring approach and input–output feedback linearization control of the induction motor (IM) in the closed-loop drive. Two kinds of faults are considered in the machine, particularly the broken rotor bars and stator inter-turn short circuit faults. This approach has been employed to detect and identify simple and mixed defects during motor operation by utilizing advanced techniques. To achieve it, two procedures are applied for the fault monitoring: The model-based strategy, which used to generate a residual speed signal to indicate the presence of possible failures, by means the high gain observer in the closed-loop drive. However, this strategy is not able to recognise the type of faults but it can be affected by the disturbances. Therefore, the neural network (NN) technique is applied in order to identify the faults and distinguish them. However, the NN required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform and fast Fourier transform is applied to extract the amplitude of the harmonics and used them as an input data set for NN. The obtained results show the efficiency of the fault monitoring system and its ability to detect and diagnosis any minor faults in a closed loop of the IM.

Keywords

Induction motor (IM) Input–output feedback linearization (IOFL) control Fault monitoring Residual speed Neural network (NN) Hilbert transform (HT) 

Abbreviations

IM

Induction motor

IOFL

Input-output feedback linearization

HGO

High gain observer

NN

Neural network

SCE

Stator current envelope

nccK′

K′ stator shorted turns

Uds, Uqs

(d, q) Axis voltages of the stator

Ids, Iqs

(d, q) Axis current components of the stator

Idr, Iqr

(d, q) Axis current components of the rotor

Ie

Short circuit ring current

[U]

Voltage vector

[I]

Current vector

[L]

Inductance matrix

[R]

Resistance matrix

R

Average radius of the air-gap

Udc

Direct voltage

Ua, Ub, Uc

Three phases voltages as, bs, cs

Ia, Ib, Ic

Three phases current as, bs, cs

U, U

(α, β) Axis voltages of the stator

ωr

Electrical rotor speed in rpm

ωref, Φref

Rotor reference speed and flux

Nbbk′

k′ broken rotor bars

y

Measurable output

u

Control variable

x

State variable

f

Fundamental frequency

s

Motor slip

Rbfk

Resistance of the bar index k

iek

Short circuit ring current of the portion k

μ0

Magnetic permeability of the air

p

Number of pole pairs

e

Air-gap mean diameter

α

Angle between two broken rotor bars

Rs

Stator resistance

Rr

Rotor resistance

Rb

Rotor bar resistance

Re

Resistance of end ring

Lb

Rotor bar inductance

Le

Inductance of end ring

Lsf

Leakage inductance of stator

Msr

Mutual inductance

Ns

Number of turns per stator phase

Nr

Number of rotor bars

l

Length of the rotor

J

Inertia moment

F

Coefficient of damping

Te, TL

Electromagnetic torque, load torque

ibk

Current of the bar k

irk

Current of the loop k

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Imadeddine Harzelli
    • 1
  • Arezki Menacer
    • 1
  • Tarek Ameid
    • 1
  1. 1.LGEB Laboratory, Electrical Engineering DepartmentBiskra UniversityBiskraAlgeria

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