Mining accident detection using machine learning methods

Francisco Santibanez, Carlos Flores, Franco Basso, Abelino Jimenez, Francisco Bravo, Felipe Nunez, Hector Luco, Luis Martnez, Angel Bentez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


Mining activity carries inherent risks in its work These risks have produced many accidents in Chilean and all mining history, some of them with fatal consequences Does the state and the environment of the mine affect workers performance and security? Do long periods without accidents generate overconfidence in workers? Do recent accidents generate insecurity in workers? These type of questions sought to be answered by the Chilean consultancy SolMat using mathematical modeling to generate a computational tool that will allow to anticipate the occurrence of accidents in order to improve the safety of workers in Minera Los Pelambres This paper generates and validates predictive models for daily and weekly prediction of accidents in all productive sectors as one large sector, and on a segmentation of the whole productive place into three specific sectors Solmat used Machine Learning techniques with supervised training, obtaining with independent testing bases results of 70% of total accuracy for the job, and 75%, 85% and 75% of accuracy for the 3 previous segmentations, being able to detect more than the half of accidents in each case For the daily case, accuracy is similar, but with less accident detection

Idioma originalInglés
Título de la publicación alojada16th IFAC Symposium on Automation in Mining, Minerals and Metal Processing, MMM 2013 - Proceedings
EditorialIFAC Secretariat
Número de páginas3
EdiciónPART 1
ISBN (versión impresa)9783902823427
EstadoPublicada - 2013
Publicado de forma externa
Evento16th IFAC Symposium on Automation in Mining, Minerals and Metal Processing, MMM 2013 - San Diego, CA, Estados Unidos
Duración: 25 ago. 201328 ago. 2013

Serie de la publicación

NombreIFAC Proceedings Volumes (IFAC-PapersOnline)
NúmeroPART 1
ISSN (versión impresa)1474-6670


Conferencia16th IFAC Symposium on Automation in Mining, Minerals and Metal Processing, MMM 2013
País/TerritorioEstados Unidos
CiudadSan Diego, CA


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