2026-04-30T02:20:53Zhttps://riubu.ubu.es/oai/request

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

Bustillo Iglesias, Andrés 81 500 Correa, Maritza b3e253ae-dedd-4b0f-9ced-2206546a508c 500 Reñones, Anibal a2413c9f-e72a-4c97-8ddb-ccff10993107 500 2016-11-09T09:32:14Z 2016-11-09T09:32:14Z 2011-03 1424-8220 http://hdl.handle.net/10259/4263 10.3390/s110302773 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. Red de Supervision y Diagnosis de Sistemas Complejos (DPI2009-06124-E) of the Spanish Ministry of Science and Innovation application/pdf eng MDPI Sensors, 2011, V. 11, n. 3, p. 2282-3400 http://dx.doi.org/10.3390/s110302773 info:eu-repo/grantAgreement/MICINN/DPI2009-06124-E Attribution 3.0 Unported http://creativecommons.org/licenses/by/3.0/ info:eu-repo/semantics/openAccess virtual sensor Bayesian classifier industrial applications tool condition monitoring multitooth-tools Informática Computer science A vrtual sensor for online fault detection of multitooth-tools info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/4263/8/license_rdf f9a6a4096bf314846151c97900b2090d MD5 8 THUMBNAIL Bustillo-Sensors_2011.pdf.jpg Bustillo-Sensors_2011.pdf.jpg IM Thumbnail image/jpeg 4492 https://riubu.ubu.es/bitstream/10259/4263/7/Bustillo-Sensors_2011.pdf.jpg 3379975563827e30bffc250cad87a7b5 MD5 7 ORIGINAL Bustillo-Sensors_2011.pdf Bustillo-Sensors_2011.pdf application/pdf 498755 https://riubu.ubu.es/bitstream/10259/4263/1/Bustillo-Sensors_2011.pdf 70a751d12311b8eeaa0f39d02eecfb74 MD5 1 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/4263/5/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 5 TEXT Bustillo-Sensors_2011.pdf.txt Bustillo-Sensors_2011.pdf.txt Extracted text text/plain 63425 https://riubu.ubu.es/bitstream/10259/4263/6/Bustillo-Sensors_2011.pdf.txt 925c630f6058ec98dc270d1ae97afeb6 MD5 6 10259/4263 oai:riubu.ubu.es:10259/4263 2021-11-10 10:38:16.984 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es RWwgYXV0b3IgY29tbyDDum5pY28gdGl0dWxhciBkZSBsb3MgZGVyZWNob3MgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIGRlIGxhIG9icmEsIG8gZGlzcG9uaWVuZG8gZGUgbG9zIGRlYmlkb3MgcGVybWlzb3MgZGUgbG9zIG90cm9zIHRpdHVsYXJlcywgc2kgbG9zIGh1YmllcmEsIHkgZW4gdmlydHVkIGRlIGxvcyBkZXJlY2hvcyBxdWUgbGUgY29uZmllcmUgbGEgbGVnaXNsYWNpw7NuIHZpZ2VudGUgc29icmUgcHJvcGllZGFkIGludGVsZWN0dWFsIHkgZGVyZWNob3MgZGUgYXV0b3IsIApBVVRPUklaQSBhIGxhIFVuaXZlcnNpZGFkIGRlIEJ1cmdvcyBhIGRpZnVuZGlyLCBkZSBtYW5lcmEgZ3JhdHVpdGEsIGVsIGNvbnRlbmlkbyBkZSBsb3MgYXJjaGl2b3MgZGlnaXRhbGVzIHF1ZSBjb3JyZXNwb25kZW4gYWwgZG9jdW1lbnRvIGRlc2NyaXRvIGFudGVyaW9ybWVudGUsIGNvbiBjYXLDoWN0ZXIgbm8gZXhjbHVzaXZvIHkgZGUgbWFuZXJhIHDDumJsaWNhIGVuIGFjY2VzbyBhYmllcnRvIGEgdHJhdsOpcyBkZSBJbnRlcm5ldCwgcGFyYSBsbyBxdWUgbGEgQmlibGlvdGVjYSBwcm9jZWRlcsOhIGEgYXJjaGl2YXJsb3MgZW4gZWwgUmVwb3NpdG9yaW8gSW5zdGl0dWNpb25hbC4gQXNpbWlzbW8gYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBkZSBCdXJnb3MgYSByZWFsaXphciBsYXMgdHJhbnNmb3JtYWNpb25lcyBuZWNlc2FyaWFzIGRlIGZvcm1hdG8sIG5vIGRlIGNvbnRlbmlkbywgcGFyYSBnYXJhbnRpemFyIGxhIHByZXNlcnZhY2nDs24geSBlbCBhY2Nlc28gZW4gZWwgZnV0dXJvLgoKRWwgYXV0b3IgZGlzcG9uZSwgZW4gdG9kbyBjYXNvLCBkZWwgZGVyZWNobyBhIHJldm9jYXIgZXN0YSBhdXRvcml6YWNpw7NuLgoKTGEgY2VzacOzbiBkZSBkZXJlY2hvcyBkZSBlc3RhIG9icmEgc2UgZW5jdWVudHJhIHN1amV0YSBhIGxhIGxlZ2lzbGFjacOzbiB2aWdlbnRlIHNvYnJlIHByb3BpZWRhZCBpbnRlbGVjdHVhbCB5IGRlcmVjaG9zIGRlIGF1dG9yLiBTdSBkaWZ1c2nDs24gZW4gZWwgUmVwb3NpdG9yaW8gc2Vyw6EgYmFqbyBsYSBtb2RhbGlkYWQgZGUgbGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyBvIGVxdWl2YWxlbnRlOiByZWNvbm9jaW1pZW50byDigJMgdXNvIG5vIGNvbWVyY2lhbCDigJMgc2luIG9icmEgZGVyaXZhZGEsIHBvciBsYSBxdWUgc2UgcGVybWl0ZSBoYWNlciBjb3BpYSwgZGlzdHJpYnVpciB5IGNvbXVuaWNhciBww7pibGljYW1lbnRlIGxhIG9icmEgc2llbXByZSBxdWUgc2UgY2l0ZSBhbCBhdXRvciwgZWwgdXNvIHF1ZSBzZSBoYWdhIGRlIGVsbGEgc2VhIG5vIGNvbWVyY2lhbCB5IG5vIHNlIGNyZWVuIG9icmFzIGRlcml2YWRhcyBhIHBhcnRpciBkZSBsYSBvcmlnaW5hbC4K