English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/154385
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar EndNote (RIS)Exportar EndNote (RIS)
Título

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

Autor Bustillo, Andres; Correa, Maritza ; Reñones, Anibal
Fecha de publicación 2-mar-2011
EditorMultidisciplinary Digital Publishing Institute
Citación Sensors 11 (3): 2773-2795 (2011)
ResumenThe installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.
URI http://hdl.handle.net/10261/154385
Identificadoresdoi: 10.3390/s110302773
Aparece en las colecciones: Colección MDPI
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
sensors-11-02773.pdf523,05 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.