2026-06-30T04:08:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/42632021-11-10T09:38:16Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

00925njm 22002777a 4500 dc Bustillo Iglesias, Andrés author Correa, Maritza author Reñones, Anibal author 2011-03 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. 1424-8220 http://hdl.handle.net/10259/4263 10.3390/s110302773 virtual sensor Bayesian classifier industrial applications tool condition monitoring multitooth-tools A vrtual sensor for online fault detection of multitooth-tools