Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

Penedo, Francisco; Haber Guerra, Rodolfo E.; Gajate, Agustín y Toro Matamoros, Raúl Mario del (2012). Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes. "IEEE Transactions on Industrial Informatics", v. 8 (n. 4); pp. 811-818. ISSN 1551-3203. https://doi.org/10.1109/TII.2012.2205699.

Descripción

Título: Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes
Autor/es:
  • Penedo, Francisco
  • Haber Guerra, Rodolfo E.
  • Gajate, Agustín
  • Toro Matamoros, Raúl Mario del
Tipo de Documento: Artículo
Título de Revista/Publicación: IEEE Transactions on Industrial Informatics
Fecha: Noviembre 2012
Volumen: 8
Materias:
Escuela: Centro de Automática y Robótica (CAR) UPM-CSIC
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

Más información

ID de Registro: 21244
Identificador DC: http://oa.upm.es/21244/
Identificador OAI: oai:oa.upm.es:21244
Identificador DOI: 10.1109/TII.2012.2205699
URL Oficial: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6224180
Depositado por: Memoria Investigacion
Depositado el: 06 Nov 2013 19:03
Ultima Modificación: 21 Abr 2016 11:23
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