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Hierarchical Temporal Memory Continuous Learning Algorithms for Fire State Determination

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Abstract

An ultimate goal of placing fire detection systems in buildings and structures is to allow for the rapid detection of fire and accurate faster than real time prediction of ensuing fire behavior so that relevant information can be delivered to the appropriate stakeholders. In the near-term, development of detection systems with decreased detection time, better discrimination against nuisance and false alarms, and real-time monitoring of the fire state is a critical interim step. Building comfort and efficiency systems are increasingly incorporating a greater quantity of sensors and these sensors are installed at a greater density than any fire sensor with the exception of the sprinkler. While currently used primarily for building management purposes, the application of these, or similar types of building sensors, for rapid fire detection, fire state determination, and fire forecasting offers great potential. This paper discusses the potential benefits of the application of Hierarchical Temporal Memory algorithms for fire state determination in a continuous learning environment based on its application to a series of live fire experiments.

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Acknowledgements

The authors would like to acknowledge materials and financial support provided by the Natural Sciences and Engineering Research Council of Canada, the UW Fire Research Facility and Fire & Risk Alliance, LLC. Also much appreciated are the considerable contributions of the University of Waterloo colleagues who assisted with the testing and provided moral support.

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Authors and Affiliations

  1. Fire and Risk Alliance, Rockville, MD, USA

    Noah L. Ryder & Justin A. Geiman

  2. Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada

    Noah L. Ryder & Elizabeth J. Weckman

Authors
  1. Noah L. Ryder
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  2. Justin A. Geiman
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  3. Elizabeth J. Weckman
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Correspondence to Noah L. Ryder.

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Ryder, N.L., Geiman, J.A. & Weckman, E.J. Hierarchical Temporal Memory Continuous Learning Algorithms for Fire State Determination. Fire Technol 57, 2905–2928 (2021). https://doi.org/10.1007/s10694-020-01055-0

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