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Frontiers in Energy

ISSN 2095-1701

ISSN 2095-1698(Online)

CN 11-6017/TK

Postal Subscription Code 80-972

2018 Impact Factor: 1.701

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Front. Energy    2016, Vol. 10 Issue (2) : 176-191    https://doi.org/10.1007/s11708-015-0386-2
RESEARCH ARTICLE
A new and best approach for early detection of rotor and stator faults in induction motors coupled to variable loads
Abderrahim ALLAL1,*(),Boukhemis CHETATE2
1. Research Laboratory on the Electrification of Industrial Enterprises, University M’Hamed Bougara, Boumerdes 35000; Institute of Sciences Technology, Chahid Hamma Lakhdar University of El-Oued, El-Oued 39000, Algeria
2. Research Laboratory on the Electrification of Industrial Enterprises, University M’Hamed Bougara, Boumerdes 35000, Algeria
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Abstract

Today, induction machines are playing, thanks to their robustness, an important role in world industries. Although they are quite reliable, they have become the target of various types of defects. Thus, for a long time, many research laboratories have been focusing their works on the theme of diagnosis in order to find the most efficient technique to predict a fault in an early stage and to avoid an unplanned stopping in the chain of production and costs ensuing. In this paper, an approach called Park’s vector product approach (PVPA) was proposed which was endowed with a dominant sensitivity in the case in which there would be rotor or stator faults. To show its high sensitivity, it was compared with the classical methods such as motor current signature analysis (MCSA) and techniques studied in recent publications such as motor square current signature analysis (MSCSA), Park’s vector square modulus (PVSM) and Park-Hilbert (P-H) (PVSMP-H). The proposed technique was based on three main steps. First, the three-phase currents of the induction motor led to a Park’s vector. Secondly, the proposed PVPA was calculated to show the distinguishing spectral signatures of each default and specific frequencies. Finally, simulation and experimental results were presented to confirm the theoretical assumptions.
Keywords induction motor      incipient broken bar      extended Park’s vector approach      spectral analysis      inter-turn short-circuit      Hilbert transform     
Corresponding Author(s): Abderrahim ALLAL   
Just Accepted Date: 30 September 2015   Online First Date: 23 November 2015    Issue Date: 27 May 2016
 Cite this article:   
Abderrahim ALLAL,Boukhemis CHETATE. A new and best approach for early detection of rotor and stator faults in induction motors coupled to variable loads[J]. Front. Energy, 2016, 10(2): 176-191.
 URL:  
https://academic.hep.com.cn/fie/EN/10.1007/s11708-015-0386-2
https://academic.hep.com.cn/fie/EN/Y2016/V10/I2/176
Approaches Faulty rotor broken bar Faulty stator inter-turn short-circuit
PVPA (1±s)2fs a 2fs±fra
PVSMP-H (1±s)3fs a fs
MSCSA (1±s)2fs a 2fs±fra
MCSA (1±2s)fsa fs±fra
PVSM 2sfs fs
Tab.1  Frequencies of a rotor or stator faults signatures
Fig.1  Simulation spectrum of PVPA for a motor loading (100%) with

(a) Broken bar; (b) 36 turns into short-circuit in phase 1

Fig.2  Simulation spectrum of MCSA for a motor loading (100%) with

(a) Broken bar; (b) 36 turns into short-circuit in phase 1

Fig.3  Simulation spectrum of MSCSA for a motor loading (100%) with

(a) Broken bar; (b) 36 turns into short-circuit in phase 1

Fig.4  Simulation spectrum of PVSM for a motor loading (100%) with

(a) Broken bar; (b) 36 turns into short-circuit in phase 1

Fig.5  Simulation spectrum of the PVSMP-H for a motor loading (100%) with

(a) Broken bar; (b) 36 turns into short-circuit in phase 1

Fig.6  Simulation sensitivity of PVPA compared to other recent diagnosis techniques (broken bar and partially broken bar)
Fig.7  Simulation sensitivity of PVPA proposed compared to other recent diagnosis techniques (with 18 and 36 turns into short-circuit in phase 1)
Approaches Faulty rotor Faulty stator
Broken bar Partially broken bar 36 turns into short-circuit in phase 1 18 turns into short-circuit in phase 1
PVPA 1 1 1 a 2
PVSMP-H 5 5 2 a 1
MSCSA 3 3 3 3
MCSA 2 2 5 5
PVSM 4 4 4 4
Tab.2  Simulation sensitivity ranking of different diagnosis approaches
Fig.8  Experimental spectrum of PVPA for a motor loading (80%) with

(a) Two broken bars; (b) 10 turns into short-circuit in phase 1

Fig.9  Experimental spectrum of MCSA for a motor loading (80%) with

(a) Two broken bars; (b) 10 turns into short-circuit in phase 1

Fig.10  Experimental spectrum of MSCSA for a motor loading (80%) with

(a) Two broken bars; (b) 10 turns into short-circuit in phase 1

Fig.11  Experimental spectrum of PVSM for a motor loading (80%) with

(a) Two broken bars; (b) 10 turns into short-circuit in phase 1

Fig.12  Experimental spectrum of PVSMP-H for a motor loading (80%) with

(a) Two broken bars; (b) 10 turns into short-circuit in phase 1

Fig.13  Experimental sensitivity of PVPA proposed compared to other recent diagnosis techniques (with two broken bars and 10 turns into short-circuit in phase 1)
Fig.14  Simplified representation showing how to make an inter-turn short-circuit in stator windings
Approaches Faulty rotor Faulty stator
Two broken bars 10 turns into short-circuit
PVPA 1 1
PVSMP-H 5 2
MSCSA 3 3
MCSA 2 4
PVSM 4 5
Tab.3  Experimental sensitivity ranking of different diagnosis approaches
SymbolParameterValue
PRated power3 kW
NrNumber of rotor bars28
pNumber of pole pairs1
NsTurns per phase360
NtStator slots36
fsSupply frequency50 Hz
VRated voltage220 V
JInertia momentum0.052 kg·m2
eAir gap length0.0005 m
RsStator resistance6 Ω
ReEnd ring resistance1.23 × 10−6 Ω
RbRotor bar resistance1.93 × 10−6 Ω
LbRotor bar leakage0.6031 × 10−6 H
LeRotor end ring leakage inductance2 × 10−9 H
LslLeakage inductance of one phase winding0.0129 H
LMachine length0.12 m
RMean radius of the air gap50 × 10−3 m
Tab.1  Table A1 Parameters of the induction motor used in the simulation
Fig.1  A1 Faults diagnosis in induction motor using PVPA
Fig.2  A2 Simulation spectrum of different approaches for a motor loading (100%, 50% and 25%) with a broken bar
Fig.3  A3 Simulation spectrum of different approaches for a motor loading (100%, 50% and 25%) with 36 turns into short-circuit in phase 1
SymbolParameterValue
PRated power3 kW
NrNumber of rotor bars28
pNumber of pole pairs2
NsTurns per phase200
fsSupply frequency50 Hz
VRated voltage220 V
Tab.1  Parameters of the induction motor used in the experimental
Fig.1  A4 Experimental spectrum of different approaches for a motor loading (80%, 60% and 20%) with two broken bars
Fig.2  
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