Dedicated hierarchy of neural networks applied to bearings degradation assessment
作者： Miguel Delgado , Giansalvo Cirrincione , Antonio Garcia Espinosa , Juan Antonio Ortega , Humberto Henao
DOI: 10.1109/DEMPED.2013.6645768
关键词: Hierarchy (mathematics) 、 Time delay neural network 、 Feature (machine learning) 、 Engineering 、 Condition monitoring 、 Machine learning 、 Pattern recognition (psychology) 、 Artificial intelligence 、 Artificial neural network 、 Set (abstract data type) 、 Data discrimination
摘要: Condition monitoring schemes, able to deal with different sources of fault are, nowadays, required by the industrial sector improve their manufacturing control systems. Pattern recognition approaches, allow identification multiple system's scenarios means relations between numerical features. The features are calculated from acquired physical magnitudes, in order characterize its behavior. However, only a reduced set used avoid computational performance limitations artificial intelligence techniques. In this sense, feature reduction techniques applied. Classical approaches analyze significance global data discrimination point view. This paper, however, proposes novel and reliable methodology exploit information contained original set, dedicated hierarchy neural networks.
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