TY - CONF
T1 - Gear Crack Level Classification by Using KNN and Time-Domain Features from Acoustic Emission Signals under Different Motor Speeds and Loads
AU - Sanchez, Rene Vinicio
AU - Lucero, Pablo
AU - Macancela, Jean Carlo
AU - Cerrada, Mariela
AU - Cabrera, Diego
AU - Vasquez, Rafael
PY - 2019/3/11
Y1 - 2019/3/11
N2 - Diagnosing failures during their initial stage is important to avoid unexpected stops and catastrophic damages, specially for gear boxes that are crucial components in industrial machines. This work addresses the classification of nine levels of crack failure severity in a gearbox. First of all, features are extracted in time domain from signals coming from an acoustic emission (AE) sensor, and then selected by using four different ranking methods. The classification stage uses the k-Nearest Neighbors (KNN) technique. The results indicate that presented levels of severity can be successfully classified with five features extracted from the AE signal for the four ranking methods.
AB - Diagnosing failures during their initial stage is important to avoid unexpected stops and catastrophic damages, specially for gear boxes that are crucial components in industrial machines. This work addresses the classification of nine levels of crack failure severity in a gearbox. First of all, features are extracted in time domain from signals coming from an acoustic emission (AE) sensor, and then selected by using four different ranking methods. The classification stage uses the k-Nearest Neighbors (KNN) technique. The results indicate that presented levels of severity can be successfully classified with five features extracted from the AE signal for the four ranking methods.
KW - Acoustic emission
KW - K-nearest neighbors
KW - fault diagnosis
KW - feature time-domain
KW - gear crack
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064119794&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85064119794&origin=inward
UR - http://www.mendeley.com/research/gear-crack-level-classification-using-knn-timedomain-features-acoustic-emission-signals-under-differ
U2 - 10.1109/SDPC.2018.8664979
DO - 10.1109/SDPC.2018.8664979
M3 - Paper
SP - 465
EP - 470
T2 - Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018
Y2 - 11 March 2019
ER -