Abstract
This paper addresses the possibility of integration of an external motor faults (e.g., phase failure, unbalanced voltage, locked rotor, undervoltage, overvoltage, phase sequence reversal of supply voltage, mechanical overload) monitoring and diagnostic technique into batch simulation with a digital protection set by using an artificial neural network (ANN) for a three-phase induction motor. The proposed set-up has been simulated using"Matlab/ Simulink" Software and tested for external motor faults. The simulated results clearly show that well-trained neural networks can precisely of early fault detection, diagnosis of external faults induction motor, also validating the proposed setup as a simple, reliable and effective protection for the three-phase induction motor fault identification scheme using an artificial neural network (ANN).
| Original language | English |
|---|---|
| Title of host publication | 2007 IEEE Power Engineering Society General Meeting, PES |
| DOIs | |
| Publication status | Published - 2007 |
| Externally published | Yes |
| Event | 2007 IEEE Power Engineering Society General Meeting, PES - Tampa, FL, United States Duration: 24 Jun 2007 → 28 Jun 2007 |
Other
| Other | 2007 IEEE Power Engineering Society General Meeting, PES |
|---|---|
| Country | United States |
| City | Tampa, FL |
| Period | 24/6/07 → 28/6/07 |
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Keywords
- Artificial neural networks
- Fault diagnostics
- Induction motor
- Rotor-cage monitoring and external motor fault
ASJC Scopus subject areas
- Energy(all)
Cite this
Monitoring and diagnosis of external faults in three phase induction motors using artificial neural network. / Eldin, El Sayed M Tag; Emara, Hassan R.; Aboul-Zahab, Essam M.; Khalil, Shady.
2007 IEEE Power Engineering Society General Meeting, PES. 2007. 4275351.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Monitoring and diagnosis of external faults in three phase induction motors using artificial neural network
AU - Eldin, El Sayed M Tag
AU - Emara, Hassan R.
AU - Aboul-Zahab, Essam M.
AU - Khalil, Shady
PY - 2007
Y1 - 2007
N2 - This paper addresses the possibility of integration of an external motor faults (e.g., phase failure, unbalanced voltage, locked rotor, undervoltage, overvoltage, phase sequence reversal of supply voltage, mechanical overload) monitoring and diagnostic technique into batch simulation with a digital protection set by using an artificial neural network (ANN) for a three-phase induction motor. The proposed set-up has been simulated using"Matlab/ Simulink" Software and tested for external motor faults. The simulated results clearly show that well-trained neural networks can precisely of early fault detection, diagnosis of external faults induction motor, also validating the proposed setup as a simple, reliable and effective protection for the three-phase induction motor fault identification scheme using an artificial neural network (ANN).
AB - This paper addresses the possibility of integration of an external motor faults (e.g., phase failure, unbalanced voltage, locked rotor, undervoltage, overvoltage, phase sequence reversal of supply voltage, mechanical overload) monitoring and diagnostic technique into batch simulation with a digital protection set by using an artificial neural network (ANN) for a three-phase induction motor. The proposed set-up has been simulated using"Matlab/ Simulink" Software and tested for external motor faults. The simulated results clearly show that well-trained neural networks can precisely of early fault detection, diagnosis of external faults induction motor, also validating the proposed setup as a simple, reliable and effective protection for the three-phase induction motor fault identification scheme using an artificial neural network (ANN).
KW - Artificial neural networks
KW - Fault diagnostics
KW - Induction motor
KW - Rotor-cage monitoring and external motor fault
UR - http://www.scopus.com/inward/record.url?scp=42549139319&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=42549139319&partnerID=8YFLogxK
U2 - 10.1109/PES.2007.385469
DO - 10.1109/PES.2007.385469
M3 - Conference contribution
AN - SCOPUS:42549139319
SN - 1424412986
SN - 9781424412983
BT - 2007 IEEE Power Engineering Society General Meeting, PES
ER -