TY - JOUR
T1 - Identification of Babbitt Damage and Excessive Clearance in Journal Bearings through an Intelligent Recognition Approach
AU - Gómez, Joel Pino
AU - Montero, Fidel E.Hernández
AU - Mancilla, Julio C.Gómez
AU - Rey, Yenny Villuendas
N1 - Publisher Copyright:
© 2021. All Rights Reserved.
PY - 2021
Y1 - 2021
N2 - Journal bearings play an important role on many rotating machines placed on industrial environments, especially in steam turbines of thermoelectric power plants. Babbitt damage (BD) and excessive clearance (C) are usual faults of steam turbine journal bearings. This paper is focused on achieving an effective identification of these faults through an intelligent recognition approach. The work was carried out through the processing of real data obtained from an industrial environment. In this work, a feature selection procedure was applied in order to choose the features more suitable to identify the faults. This feature selection procedure was performed through the computation of typical testors, which allows working with both quantitative and qualitative features. The classification tasks were carried out by using Nearest Neighbors, Voting Algorithm, Naïve Associative Classifier and Assisted Classification for Imbalance Data techniques. Several performance measures were computed and used in order to assess the classification effectiveness. The achieved results (e.g., six performance measures were above 0.998) showed the convenience of applying pattern recognition techniques to the automatic identification of BD and C.
AB - Journal bearings play an important role on many rotating machines placed on industrial environments, especially in steam turbines of thermoelectric power plants. Babbitt damage (BD) and excessive clearance (C) are usual faults of steam turbine journal bearings. This paper is focused on achieving an effective identification of these faults through an intelligent recognition approach. The work was carried out through the processing of real data obtained from an industrial environment. In this work, a feature selection procedure was applied in order to choose the features more suitable to identify the faults. This feature selection procedure was performed through the computation of typical testors, which allows working with both quantitative and qualitative features. The classification tasks were carried out by using Nearest Neighbors, Voting Algorithm, Naïve Associative Classifier and Assisted Classification for Imbalance Data techniques. Several performance measures were computed and used in order to assess the classification effectiveness. The achieved results (e.g., six performance measures were above 0.998) showed the convenience of applying pattern recognition techniques to the automatic identification of BD and C.
KW - Journal bearing
KW - babbitt damage
KW - excessive clearance
KW - fault identification
KW - feature selection
KW - supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85105818902&partnerID=8YFLogxK
U2 - 10.14569/IJACSA.2021.0120467
DO - 10.14569/IJACSA.2021.0120467
M3 - Artículo
AN - SCOPUS:85105818902
SN - 2158-107X
VL - 12
SP - 526
EP - 533
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 4
ER -