IoT to improve the efficacy of batteries in fire detection systems

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16/10/2023

Researchers from the Electronic and Biomedical Instrumentation (IEB) group of the Biomedical Engineering Research Centre (CREB) at the UPC are working with the company Detnov Security SL on a project to improve battery performance in fire detection systems.


Fires in buildings are a great threat due to the frequency of their occurrence and their consequences. They cause great concern and generate the need to adopt effective protection measures. Over the years, various technical solutions have been developed that enable both fire detection and prevention of damage.

Fire protection systems are equipped with batteries that ensure functioning in the case of power cuts. Usually, they contain lead-acid batteries with a power source that keeps them charged. However, traditional chargers have limitations in their capacity to accurately monitor the vital parameters of batteries, which can lead to inefficient charge patterns. This means that they have to be inspected and replaced frequently to avoid safety problems.

Through the incorporation of internet of things (IoT) technologies, battery chargers can use cloud computing to estimate battery parameters, optimise the charge and even introduce new functionalities such as the prediction of ageing. In addition, the incorporation of these technologies plays a key role in reducing the environmental impact associated with lead-acid batteries in fire detection systems, by increasing their useful life from 5 to 10 years.

Although the use of IoT in fire detection systems is increasing, there is still a lack of research on their use to prevent faults in the system. To address this shortfall, the research and development team of the company Detnov Security SL, in collaboration with the Biomedical Engineering Research Centre (CREB) at the Universitat Politècnica de Catalunya - BarcelonaTech (UPC), is working on a project to apply improvements in fire detection systems and to develop data gathering techniques focused on predicting battery performance.

The aim of the project is therefore to improve the management, reliability and preventive maintenance of batteries, by applying IoT techniques and data science to internal variables measured in these systems throughout their entire life cycle. The objective is to lengthen their useful life and reduce their environmental impact.

In particular, the aim is to undertake a study of all the unexploited data that are available in the network of fire protection systems, and a study of appropriate methods for gathering and transmitting these data, to improve the system’s features.

This project combines knowledge of the fire detection product, knowledge of the physical principles that govern the ageing of electronic components, and knowledge of data management, transmission and processing. In the project, sufficient information will be gathered to model the ageing of elements of the system, predict faults and enable the planning of preventive maintenance, to avoid corrective maintenance interventions as far as possible. In this way, the objective is to improve the focus of preventive maintenance, reduce travel and ensure that a building’s installations function under optimum conditions throughout the time that the system is in operation.

The project includes:

  • Undertaking a study of the information available in the fire prevention systems that currently is not being used appropriately.
  • Propose the variations in electronic circuits that are needed to access these data.
  • Resolve challenges of gathering and transmitting measurements of interest.
  • Process data obtained to gain information of interest.
  • Generate improvements in maintenance and detection due to the data that are gathered.

Budget and funding

The research project ‘Smart sensing and monitoring of fire detection systems’ has a budget of €33,960, due to a grant awarded by the Catalan Government’s Agency for Management of University and Research Grants (AGAUR). The project has a three-year duration (December 2022 – December 2025).


 
 

 
 

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