Open AccessGearbox Condition Monitoring Using Advanced Classifiers
Author(s) -
P. Večeř,
M. Kreidl,
Radislav Šmı́d
Publication year - 2010
Publication title -
acta polytechnica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.207
H-Index - 15
eISSN - 1805-2363
pISSN - 1210-2709
DOI - 10.14311/1149
Subject(s) - adaptive neuro fuzzy inference system , automotive industry , computer science , artificial neural network , condition monitoring , artificial intelligence , quality (philosophy) , fuzzy logic , neuro fuzzy , data mining , machine learning , pattern recognition (psychology) , automotive engineering , engineering , fuzzy control system , philosophy , electrical engineering , epistemology , aerospace engineering
New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS). Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing.