Document details

Smart Sensor Data Acquisition in trains

Author(s): Pereira, António João Marques de Andrade

Date: 2017

Persistent ID: http://hdl.handle.net/10362/31865

Origin: Repositório Institucional da UNL

Subject(s): distortion; noise; distributed systems; Smart Systems; Smart Sensors; data acquisition; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática


Description

Whether for work or leisure, we see a large number of people traveling by train every day. In order to ensure the comfort and safety of passengers, it must be checked whether the composition is working normally. For this purpose, a constant monitoring of a train must be done, followed by a diagnosis of the com-position, prediction of failures and production of alarms in the event of any anomaly. To perform monitoring on a train, it is necessary to collect data from sensors distributed along its carriages and send them to a software system that performs the diagnosis of the composition in a fast and efficient way. The description of the activities necessary for monitoring of a train imme-diately refers to topics such as distributed systems, since the intended system will have to integrate several sensors distributed along the train, or Smart Systems, since each sensor must have the capacity to not only acquire data, but also trans-mit it, preferably, wirelessly. However, there are some obstacles to the implementation of such a system. Firstly, the existence of sources of distortions and noise in the medium interferes both in the acquisition and transmission of data and secondly the fact that the sensors distributed along the train are not prepared to be connected directly to a software system. This dissertation seeks to find a solution for the problems described by im-plementing a data acquisition system that is distributed and takes advantage of the current technologies of low-cost sensor nodes as well as web technologies for sensor networks.

Document Type Master thesis
Language English
Advisor(s) Pimentão, João; Sousa, Pedro
Contributor(s) RUN
facebook logo  linkedin logo  twitter logo 
mendeley logo

Related documents

No related documents