Data Fusion of Georeferenced Events for Detection of Hazardous Areas

Sergio Onofre, Joao Gomes, Joao Paulo Pimentao, Pedro Alexandre Sousa

Research output: Contribution to conferencePaper

Abstract

When dealing with events in moving vehicles, which can occur over widespread areas, it is difficult to identify sources that do not derive from material fatigue, but from situations that occur in specific spots. Considering a railway system, problems could occur in trains, not because of train’s equipment failure, but because the train is crossing a specific location. This paper presents a new smart system being developed that is able to generate geo-located sensor-data; transmit it for smart processing and fusing to the inference engine being built to correlate the data, and drill-down the information. Using a statistical approach within the inference engine, it is possible to combine results collected over long periods of time in a “heat-map” of frequent fault areas, mapping faulty events to detect hazardous locations using georeferenced sensor data, collected from several trains that will be integrated in these maps to infer high probability risk areas.
Original languageEnglish
Pages81-89
Number of pages9
DOIs
Publication statusPublished - 2017
Event8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS) - Costa de Caparica, Portugal
Duration: 3 May 20175 May 2017

Conference

Conference8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS)
CountryPortugal
CityCosta de Caparica
Period3/05/175/05/17

Keywords

  • Data-fusing
  • Smart system
  • Industry 4.0
  • IoT
  • Event geographic position
  • Data correlation
  • Forecast

Cite this

Onofre, S., Gomes, J., Pimentao, J. P., & Sousa, P. A. (2017). Data Fusion of Georeferenced Events for Detection of Hazardous Areas. 81-89. Paper presented at 8th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Costa de Caparica, Portugal. https://doi.org/10.1007/978-3-319-56077-9_7