Sensor based feature evaluation and selection using the self-organizing map

Rui Silva


This paper presents a new approach to sensor based feature evaluation and selection for modelling purposes using a Self-Organizing Map. Self-Organizing Maps perform classification in a non-supervised fashion performing vector quantization and therefore place similar vectors close together in the two dimensional output space. The unsupervised process leads to the self organization of modelling with no previous knowledge of what is being modelled and therefore it does not model a predetermined environment. Taking the above into account feature selection was performed by analysing the contributions of different sensor based features, carrying large quantities of noise, towards tool wear classification. It was found that some of the features, not previously evaluated and justified, have a strong contribution towards tool wear classification. It is demonstrated that the use of the self-organizing map can be used to quantitatively evaluate the contribution of features towards neural network modelling of systems in the presence of noisy data.



Feature selection, Self-organizing map, Condition monitoring, Tool wear.

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