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An automatic rotor bar fault diagnosis using fuzzy logic and DWT-energy for backstepping control driven induction motor in low-speed operation

  • Fuzzy systems and their mathematics
  • Published:
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Abstract

The contribution of rotor bar fault diagnosis and classification in low-speed induction motor drives under varying load torques has been a topic of limited discussion in the literature. Within this respect, the present paper will be discussing this condition. To ensure control performance and investigate the diagnosis process in low-speed operations, the backstepping algorithm is utilized to handle uncertainties. The discrete wavelet transform is employed to detect faults and decompose signals at various levels, while the fuzzy logic algorithm is applied to classify the fault severity. This study's novelty lies in evaluating fault severity for no/low-load conditions by using the energy approximation obtained from the discrete wavelet transforms of the speed regulator's output signal. This approximation is then used as input for the fuzzy fault classification algorithm. The control algorithm and fault diagnosis are validated experimentally using MATLAB/Simulink with a real-time interface based on dSpace 1104 implementation. The results obtained from this procedure demonstrate successful fault detection and severity classification using both the discrete wavelet approximation and its energy eigenvalue.

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Data availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. The authors have used the data of the machine applied in this study as shown in “Appendix”. These data and parameters have been obtained by both nameplates of the machine and parametric identification.

Abbreviations

IM:

Induction machine

BC:

Backstepping control

DWT:

Discrete wavelet transform

FFC:

Fuzzy fault classification

OSSR:

Output signal of speed regulator

V a, V b, V c :

Three phases reference voltages a, b, c

S a, S b, S c :

Switching states of the inverter a, b, c

V ds, V qs :

(D,q) Axis voltages of the stator

i a, i b, i c :

Three phases currents a, b, c of the stator

i ds, i qs :

(D,q) Axis current components of the stator

Γ :

Lyapunov function

n p :

Number of pole pairs

ω :

Rotor speed

M sr :

Mutual inductance

L s :

Stator inductance

L r :

Rotor inductance

ϕ rd :

Direct rotor flux

R s :

Stator resistance

R r :

Rotor resistance

f sa :

Sampling rate

f s :

Power frequency

f rb :

Rotor bar fault frequency

s :

Slip

L :

Low-pass filter

H :

High-pass filter

J :

Inertia moment

T L :

Load torque

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Funding

The authors declare that they have no supporting funding financial.

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Authors and Affiliations

  1. Laboratoire des Systèmes Électrotechniques et Environnement (LSEE) - UR 4025, Université d’Artois, 62400, Béthune, France

    Tarek Ameid & Younes Azzoug

  2. Department of Electrical Engineering, Laboratory of Electrical Engineering of Biskra (LGEB), Mohamed Khider University, Biskra, Algeria

    Tarek Ameid, Abdelkarim Ammar, Younes Azzoug & Mohamed Chebaani

  3. Signals and Systems Laboratory (LSS), Institute of Electrical and Electronic Engineering, University of M’hamed Bougara of Boumerdes, Boumerdes, Algeria

    Abdelkarim Ammar

  4. LPMRN Laboratory, Department of Electromechanics, Bachir ElIbrahimi University, Bordj Bou Arreridj, Algeria

    Hicham Talhaoui

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  1. Tarek Ameid
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  2. Abdelkarim Ammar
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  3. Hicham Talhaoui
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  4. Younes Azzoug
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  5. Mohamed Chebaani
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Correspondence to Tarek Ameid.

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Appendix

Appendix

Type

Three-phase squirrel cage

Rated power

1.1 kW

Supply frequency

50 Hz

Number of pole pairs

2

Rated speed

1450 rpm

Rated stator current

2.5 A

Rated RMS phase voltage

400 V

Connection

Υ

Stator resistance

6.75Ω

Rotor resistance

6.21Ω

Stator inductance

0.5192 H

Rotor inductance

0.5192 H

Mutual inductance

0.4757 H

Number of rotor bars

46

Inertia moment

0.0124 kg m2

Damping coefficient

0.0029 N  m/rad/s

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Ameid, T., Ammar, A., Talhaoui, H. et al. An automatic rotor bar fault diagnosis using fuzzy logic and DWT-energy for backstepping control driven induction motor in low-speed operation. Soft Comput 27, 10411–10426 (2023). https://doi.org/10.1007/s00500-023-08443-y

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