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.
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Abbreviations
- IM:
-
Induction motor
- IOFL:
-
Input-output feedback linearization
- HGO:
-
High gain observer
- NN:
-
Neural network
- SCE:
-
Stator current envelope
- n ccK′ :
-
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
- I e :
-
Short circuit ring current
- [U]:
-
Voltage vector
- [I]:
-
Current vector
- [L]:
-
Inductance matrix
- [R]:
-
Resistance matrix
- R :
-
Average radius of the air-gap
- U dc :
-
Direct voltage
- Ua, Ub, Uc :
-
Three phases voltages as, bs, cs
- Ia, Ib, Ic :
-
Three phases current as, bs, cs
- Usα, Usβ :
-
(α, β) Axis voltages of the stator
- ω r :
-
Electrical rotor speed in rpm
- ωref, Φref :
-
Rotor reference speed and flux
- N bbk′ :
-
k′ broken rotor bars
- y :
-
Measurable output
- u :
-
Control variable
- x :
-
State variable
- f :
-
Fundamental frequency
- s :
-
Motor slip
- R bfk :
-
Resistance of the bar index k
- i ek :
-
Short circuit ring current of the portion k
- μ 0 :
-
Magnetic permeability of the air
- p :
-
Number of pole pairs
- e :
-
Air-gap mean diameter
- α :
-
Angle between two broken rotor bars
- R s :
-
Stator resistance
- R r :
-
Rotor resistance
- R b :
-
Rotor bar resistance
- R e :
-
Resistance of end ring
- L b :
-
Rotor bar inductance
- L e :
-
Inductance of end ring
- L sf :
-
Leakage inductance of stator
- M sr :
-
Mutual inductance
- N s :
-
Number of turns per stator phase
- N r :
-
Number of rotor bars
- l :
-
Length of the rotor
- J :
-
Inertia moment
- F :
-
Coefficient of damping
- Te, TL :
-
Electromagnetic torque, load torque
- i bk :
-
Current of the bar k
- i rk :
-
Current of the loop k
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Appendix: Parameters for the simulation of the IM
Appendix: Parameters for the simulation of the IM
P n | Output power | 1.1 kW |
U s | Stator voltage | 220 V |
p | Number of pole pairs | 1 |
R s | Stator resistance | 7.58 Ω |
R r | Rotor resistance | 6.3 Ω |
R b | Rotor bar resistance | 0.15 mΩ |
R e | Resistance of end ring segment | 0.15 mΩ |
L b | Rotor bar inductance | 0.1 μH |
L e | Inductance of end ring | 0.1 μH |
L sf | Leakage inductance of stator | 26.5 mH |
M sr | Mutual inductance | 46.42 mH |
N s | Number of turns per stator phase | 160 |
N r | Number of rotor bars | 16 |
R | Average radius of the air-gap | 35.7 mm |
l | Length of the rotor | 65 mm |
e | Air-gap mean diameter | 2.5 mm |
J | Inertia moment | 0.0054 kg m2 |
F | Coefficient of damping | 0.0029 Nm/rad/s |
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Harzelli, I., Menacer, A. & Ameid, T. A fault monitoring approach using model-based and neural network techniques applied to input–output feedback linearization control induction motor. J Ambient Intell Human Comput 11, 2519–2538 (2020). https://doi.org/10.1007/s12652-019-01307-0
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DOI: https://doi.org/10.1007/s12652-019-01307-0