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Sensors 2011, 11(3), 2773-2795; doi:10.3390/s110302773

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

1
Department of Civil Engineering, University of Burgos, C/ Francisco de Vitoria s/n, 09006, Burgos, Spain
2
Automatic and Robotic Centre (CAR), UPM-CSIC, Km. 0.200 La Poveda, Arganda del Rey, 28500, Madrid, Spain
3
CARTIF Foundation, Parque Tecnologico de Boecillo 205, 47151, Valladolid, Spain
*
Author to whom correspondence should be addressed.
Received: 7 January 2011 / Revised: 12 February 2011 / Accepted: 14 February 2011 / Published: 2 March 2011
(This article belongs to the Section Physical Sensors)
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Abstract

The 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. View Full-Text
Keywords: virtual sensor; Bayesian classifier; industrial applications; tool condition monitoring; multitooth-tools virtual sensor; Bayesian classifier; industrial applications; tool condition monitoring; multitooth-tools
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Bustillo, A.; Correa, M.; Reñones, A. A Virtual Sensor for Online Fault Detection of Multitooth-Tools. Sensors 2011, 11, 2773-2795.

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