TY - GEN
T1 - Misalignment identification in induction motors using orbital pattern analysis
AU - Carbajal-Hernández, José Juan
AU - Sańchez-Fernández, Luis Pastor
AU - Landassuri-Moreno, Victor Manuel
AU - De Medel-Juárez, José Jesús
PY - 2013
Y1 - 2013
N2 - Induction motors are the most common engine used worldwide. When they are summited to extensive working journals, e.g. in industry, faults may appear, generating a performance reduction on them. Several works have been focused on detecting early mechanical and electrical faults before damage appears in the motor. However, the main drawback of them is the complexity on the motor's signal mathematical processing. In this paper, a new methodology is proposed for detecting misalignment faults in induction motors. Through signal vibration and orbital analysis, misalignment faults are studied, generating characteristically patterns that are used for fault identification. Artificial Neural Networks are evolved with an evolutionary algorithm for misalignment pattern recognition, using two databases (training and recovering respectively). The results obtained, indicate a good performance of Artificial Neural Networks with low confusion rates, using experimental patterns obtained from real situations where motors present a certain level of misalignment.
AB - Induction motors are the most common engine used worldwide. When they are summited to extensive working journals, e.g. in industry, faults may appear, generating a performance reduction on them. Several works have been focused on detecting early mechanical and electrical faults before damage appears in the motor. However, the main drawback of them is the complexity on the motor's signal mathematical processing. In this paper, a new methodology is proposed for detecting misalignment faults in induction motors. Through signal vibration and orbital analysis, misalignment faults are studied, generating characteristically patterns that are used for fault identification. Artificial Neural Networks are evolved with an evolutionary algorithm for misalignment pattern recognition, using two databases (training and recovering respectively). The results obtained, indicate a good performance of Artificial Neural Networks with low confusion rates, using experimental patterns obtained from real situations where motors present a certain level of misalignment.
KW - Misalignment
KW - Motor fault
KW - Neural networks evolution
KW - Orbital analysis
KW - Patterns recognition
UR - http://www.scopus.com/inward/record.url?scp=84893170843&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-41827-3_7
DO - 10.1007/978-3-642-41827-3_7
M3 - Contribución a la conferencia
SN - 9783642418266
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 50
EP - 58
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings
T2 - 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013
Y2 - 20 November 2013 through 23 November 2013
ER -