TY - GEN
T1 - Dedicated hierarchy of neural networks applied to bearings degradation assessment
AU - Delgado, Miguel
AU - Cirrincione, Giansalvo
AU - Espinosa, Antonio Garcia
AU - Ortega, Juan Antonio
AU - Henao, Humberto
PY - 2013
Y1 - 2013
N2 - Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple system's scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks.
AB - Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector to improve their manufacturing control systems. Pattern recognition approaches, allow the identification of multiple system's scenarios by means the relations between numerical features. The numerical features are calculated from acquired physical magnitudes, in order to characterize its behavior. However, only a reduced set of numerical features are used in order to avoid computational performance limitations of the artificial intelligence techniques. In this sense, feature reduction techniques are applied. Classical approaches analyze the features significance from a global data discrimination point of view. This paper, however, proposes a novel and reliable methodology to exploit the information contained in the original features set, by means a dedicated hierarchy of neural networks.
KW - Ball bearings
KW - Classification algorithms
KW - Curvilinear Component Analysis
KW - Discriminant Analysis
KW - Fault diagnosis
KW - Motor Fault detection
KW - Neural Networks
KW - Time domain analysis
KW - Vibrations
UR - https://www.scopus.com/pages/publications/84891116519
U2 - 10.1109/DEMPED.2013.6645768
DO - 10.1109/DEMPED.2013.6645768
M3 - Conference contribution
AN - SCOPUS:84891116519
SN - 9781479900251
T3 - Proceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013
SP - 544
EP - 551
BT - Proceedings - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013
PB - IEEE Computer Society
T2 - 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, SDEMPED 2013
Y2 - 27 August 2013 through 30 August 2013
ER -
