Abstract
In this paper, a technique primarily based on the discrete wavelet transform (DWT) and the slip associated with a neural network (NN) for classification and fault detection of broken rotor bars in an induction machine has been proposed. The calculated energy in each decomposition level obtained by the DWT analysis and the slip of the motor are used as input to the classifier in order to diagnose the healthy and faulty classes. The advantage of this method lays in the use of one single current sensor with the slip factor in order to detect the presence of the fault and identify the number of broken bars under different load conditions. The DWT analysis is proposed to overcome the limitation of the Fourier analysis and it is mainly adapted for the non-stationary signals. Moreover, the slip factor is applied as a second input on the classifier of NN to eliminate the problem of low-load. Feed-forward multi-layer Perceptron neural network is chosen as a technique to classify the fault in an induction machine. This technique is performed and validated by experimental data.
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The authors would like to thank Professor Menacer Arezki at the LGEB Laboratory, Biskra, Algeria, for his help.
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Appendix
Appendix
1.1 Simulation parameter
PnM | rated power | 1.1 kW |
IM | rated current | 2 0.5/4.3 A |
VM | rated line voltage | 230 V |
fN | frequency | 50 Hz |
pM | number of pole pairs | 1 |
RM | average diameter at the air gap | 35.76 10–3 m |
lM | length of the rotor | 65 10–3 m |
eM | air-gap diameter | 0.2 10–3 m |
NrM | number of bars | 16 |
NsM | number of turns per phase | 160 |
RsM | resistance of a stator phase | 7.58 Ω |
RbM | resistance of a rotor bar | 150 10–6 Ω |
ReM | resistance of a ring portion | 150 10–6 Ω |
LeM | short circuit ring leakage inductance | 0.1 10–6 H |
LbM | leakage inductance of a rotor bar | 0.1 10–6 H |
LsfM | stator leakage inductance | 26.5 10–3 H |
JM | inertia moment | 5.4 10–3 kg.m2 |
1.2 Experimental parameter
PnM | rated power | 1.1 kW |
IM | nominal current | 2 0.5/4.3 A |
VM | nominal line voltage | 400/230 V |
pM | number of pole pairs | 2 |
Υ | connection | |
JM | inertia moment | 0.0124 kg.m2 |
FM | damping coefficient | 0.0029 N.m/rad/s |
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Talhaoui, H., Ameid, T. & Kessal, A. Energy eigenvalues and neural network analysis for broken bars fault diagnosis in induction machine under variable load: experimental study. J Ambient Intell Human Comput 13, 2651–2665 (2022). https://doi.org/10.1007/s12652-021-03172-2
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DOI: https://doi.org/10.1007/s12652-021-03172-2