TY - GEN
T1 - Variable selection for journal bearing faults diagnostic through logical combinatorial pattern recognition
AU - Gómez, Joel Pino
AU - Hernández Montero, Fidel E.
AU - Gómez Mancilla, Julio C.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Experts in industrial diagnostics can provide essential information, expressed in mixed variables (quantitative and qualitative) about journal bearing faults. However, researches on feature selection for fault diagnostic applications discard the important qualitative expertise. This work focuses on the identification of the most important features, quantitative and also qualitative, for fault identification in a steam turbine journal bearings through the application of logical combinatorial pattern recognition. The value sets that support this research come from diagnostics and maintenance reports from an active thermoelectric power plant. Mixed data processing was accomplished by means of logical combinatorial pattern recognition tools. Confusion of raw features set was obtained by employing different comparison criterion. Subsequently, testors and typical testors were identified and the informational weight of features in typical testors was also computed. The high importance of the mixed features that came from the expert knowledge was revealed by the obtained achievements.
AB - Experts in industrial diagnostics can provide essential information, expressed in mixed variables (quantitative and qualitative) about journal bearing faults. However, researches on feature selection for fault diagnostic applications discard the important qualitative expertise. This work focuses on the identification of the most important features, quantitative and also qualitative, for fault identification in a steam turbine journal bearings through the application of logical combinatorial pattern recognition. The value sets that support this research come from diagnostics and maintenance reports from an active thermoelectric power plant. Mixed data processing was accomplished by means of logical combinatorial pattern recognition tools. Confusion of raw features set was obtained by employing different comparison criterion. Subsequently, testors and typical testors were identified and the informational weight of features in typical testors was also computed. The high importance of the mixed features that came from the expert knowledge was revealed by the obtained achievements.
KW - Confusion
KW - Feature selection
KW - Journal bearing
KW - Mixed features
KW - Testor
UR - http://www.scopus.com/inward/record.url?scp=85057268191&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01132-1_34
DO - 10.1007/978-3-030-01132-1_34
M3 - Contribución a la conferencia
SN - 9783030011314
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 299
EP - 306
BT - Progress in Artificial Intelligence and Pattern Recognition - 6th International Workshop, IWAIPR 2018, Proceedings
A2 - Heredia, Yanio Hernández
A2 - Núñez, Vladimir Milián
A2 - Shulcloper, José Ruiz
PB - Springer Verlag
T2 - 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018
Y2 - 24 September 2018 through 26 September 2018
ER -