How is the project doing?
The project started in December 2015 and data is now flowing from the University’s Building Management System (BMS) through to Argand’s platform. The data is being stored, managed and processed to support the development of the statistical analysis that will underpin the project’s innovation. In addition, the initial analytics visualisation software has been developed and is in beta with the University’s data.
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READ Argand's latest BARM Blog here...
argandsolutions.com/electrical-asset-risk-management/
argandsolutions.com/energy-asset-risk-management/
NEW!
BARM Blog The Commercial Building Asset Risk Management (BARM) project, aims to collect and analyse data from large buildings’ Building Management Systems (BMS) and display this as a real time information for facilities mangers to monitor their buildings more effectively. It will give them access to user-friendly data regarding energy usage and alert them to potential issues with assets ahead of failures. It is a collaboration between Argand Solutions Ltd. and the University of East London, funded by EPSRC and InnovateUK. The main theme of this project is to reveal how unlocking data from an existing BMS can provide greater understanding of the economic benefits of investing in predictive maintenance of electrical assets in a commercial setting. All electrical assets produce power data signatures which are currently under-valued and often unused by building management. However, these signatures can be exploited and transformed into power quality data which can then be analysed and used to indicate performance, degradation, maintenance needs and lifespan of buildings’ assets. Argand have developed a cloud-based software solution designed to run in combination with a building’s existing BMS. By monitoring the power quality data and developing predictive maintenance strategies through the BARM project software, businesses will be able to proactively respond to actual energy system risk factors and lengthen the lifetime of their buildings and the electrical assets that operate in them. This reduces the risk of developing business critical problems which cost time and money to remedy. The predictive maintenance approach is cost beneficial with lower maintenance costs and a high return on investment. The BARM software will allow for electrical power quality data to be gathered and analysed without the need for any further capital spend on metering requirements and requires no additional high level user training for the end user. It is being trialled in the University of East London’s sports dock building, based in Docklands, with interesting results already being achieved. |