Data Mining Application of a Predictive Maintenance System Fire Detection

Authors

  • José A. Fuentes Quiñonez Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería Electrónica y Eléctrica. Lima, Perú
  • Augusto Cortez Vásquez Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería de Sistemas e Informática. Lima, Perú
  • Daniel H. Jara Rivas Universidad Nacional Mayor de San Marcos, Facultad de Ingeniería Electrónica y Eléctrica. Lima, Perú

Keywords:

data mining, device, smoke detector, addressable detector, analog detector, conventional detector, log report

Abstract

When treatment is given to large data volumes, it is necessary to use a set of computational techniques known as data mining. In particular, the medium-sized building smoke detection devices can far exceed the one hundred units and fire detection devices require periodic maintenance according to certain pre-established control patterns. To fulfill this purpose requires a crucial tool to optimize the maintenance of such devices to determine which devices require maintenance. This article discusses the concepts of data mining and its application in the maintenance of security systems for fire detection in medium and large infrastructure details. It also proposes a methodology to forecast the trend for maintenance and fault smoke detectors of an intelligent system, we will base it on automatic reports generated by the system panel and written reports of the maintenance work.

Downloads

Published

2014-06-16

Issue

Section

Original papers

How to Cite

Data Mining Application of a Predictive Maintenance System Fire Detection. (2014). Electrónica - UNMSM, 17(1), 34-41. https://revistasinvestigacion.unmsm.edu.pe/index.php/electron/article/view/15263