dc.creator | Herranz, Gorka (1) | |
dc.creator | Antolínez, Alfonso (1) | |
dc.creator | Escartín, Javier | |
dc.creator | Arregi, Amaia | |
dc.creator | Gerrikagoitia, Jon Kepa | |
dc.date.accessioned | 2020-07-09T07:25:18Z | |
dc.date.accessioned | 2023-03-07T19:27:24Z | |
dc.date.available | 2020-07-09T07:25:18Z | |
dc.date.available | 2023-03-07T19:27:24Z | |
dc.date.created | 2020-07-09T07:25:18Z | |
dc.identifier | 25044494 | |
dc.identifier | https://reunir.unir.net/handle/123456789/10241 | |
dc.identifier | https://doi.org/10.3390/jmmp3040097 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5904579 | |
dc.description.abstract | This work presents a new methodology for machine tools anomaly detection via operational
data processing. The previous methodology has been field tested on a milling-boring machine
in a real production environment. This paper also describes the data acquisition process, as
well as the technical architecture needed for data processing. Subsequently, a technique for
operational machine data segmentation based on dynamic time warping and hierarchical clustering
is introduced. The formerly mentioned data segmentation and analysis technique allows for
machine tools anomaly detection thanks to comparison between near real-time machine operational
information, coming from strategically positioned sensors and outcomes collected from previous
production cycles. Anomaly detection techniques shown in this article could achieve significant
production improvements: “zero-defect manufacturing”, boosting factory efficiency, production
plans scrap minimization, improvement of product quality, and the enhancement of overall
equipment productivity. | |
dc.language | eng | |
dc.publisher | Journal of Manufacturing and Materials Processing | |
dc.relation | ;vol. 3, nº 4 | |
dc.relation | https://www.mdpi.com/2504-4494/3/4/97 | |
dc.rights | openAccess | |
dc.subject | machine tools | |
dc.subject | anomaly detection | |
dc.subject | data science | |
dc.subject | Industry 4.0 | |
dc.subject | predictive maintenance | |
dc.subject | Internet of Things | |
dc.subject | Scopus | |
dc.subject | Emerging | |
dc.title | Machine tools anomaly detection through nearly real-time data analysis | |
dc.type | Articulo Revista Indexada | |