Research into multi-sensor detector capabilities and false alarm reduction

Transcription

1 Research into multi-sensor detector capabilities and false alarm reduction Raman Chagger Principal Consultant, Fire Safety Group, BRE BRE Fire Conference, 18 th September 2018 Part of the BRE Trust

2 Introduction Losses from false fire alarms ~ 1 billion/year in the UK False alarms have consequences: FRS drain on/diverted resources Businesses disruptions/loss of productivity Public - reduced confidence/frustration Road traffic accidents

3 False alarm studies Study 1: The causes of false fire alarms in buildings Study 2: Live investigations of false fire alarms

4 False alarm studies Study 1: KCL 6 recommendations Potentially 49.5% reduction through the greater use of multi-sensors. Study 1: BMKFA Potentially 27.0% reduction through the greater use of multi-sensors. Study 2: SFRS 35 recommendations Potentially 35.1% reduction through the greater use of multi-sensors.

5 Multi-sensor detectors Multi-sensors utilise a number of sensors to provide more reliable detection Research with SFRS identified that no false alarms were caused from multi-sensor detectors One of the recommendations Further research is required to identify multisensors performance variabilities and capabilities. Heat Optical smoke Carbon Monoxide Photo courtesy of Tyco Fire Protection Products

6 Optical/heat multi-sensor detector research The BRE Trust, 12 manufacturers and the Fire Industry Association started a 3 phase research project Phase 1: Review of multi-sensor capabilities and variabilities. Identify tests Phase 2: Performing a broad range of test fires (compare with optical) Phase 3: Performing a broad range of common false alarm tests to identify resistance of multi-sensors. Aim of identifying relative benefits of multi-sensors over optical detectors

7 Video

8 Phase 1: Identification of false alarm tests False alarm tests explored: Dust (long term) Dust (short term) Smoke from cooking Steam Condensation Aerosols Smoke from toaster Cigarette smoke Synthetic (smoke machines) Insects Thermal shock

9 Phase 1: Development of false alarm tests Dust (long term) Dust (short term) Smoke from cooking Steam Condensation Aerosols (hairspray/deodorant) Smoke from toaster Cigarette smoke Synthetic (smoke machines) Insects Thermal shock Dust Water mist Aerosol Toast Cooking

10 Phase 1: Identification of fire tests Utilised the methodology from previous work into test fires for smoke alarms and detectors Testfire m:y Δ t (db/m) ( C) ABS (S) Flame retardant PU foam (S) TF2 Wood(S) TF3 Cotton (S) TF4 PU foam (F) TF5N-heptane(F) TF8Decalin(F) Nylon (F) Flame retardant PU foam (F) TF1wooden crib (F) (F) = Flaming; (S) = Smouldering

11 Phase 2: Fire tests 36 types of different optical heat multi-sensor detectors tested alongside 2 reference optical smoke detectors Multi-sensors categorised in terms of their false alarm resistance as basic, intermediate or advanced

12 Phase 2: Fire tests- Smouldering Flame Retardant PU Foam

13 Phase 3: False alarm tests- Toast

14 Phase 3: False alarm tests- Water mist

15 Phase 3: False alarm tests- Aerosol

16 Phase 3: False alarm tests- overview Multi-sensor response normalised to optical (%) 250% 200% 150% 100% 50% 0% 216% Multi-sensor detector average Optical smoke devices average 182% 191% Toast (db/m) Cooking (db/m) Water mist (db/m) False alarm test 248% Dust (db/m) 207% Aerosol (sec. db/m)

17 Phase 3: False alarm tests- example scenarios

18 Phase 3: False alarm and fire test comparison

19 Conclusion - Research has demonstrated that multisensor detectors can have the same response to fire but delayed response to false alarms - FIA and BRE are working towards the development of a Loss Prevention Standard for False Alarm Resistance - FIA guidance on false alarm reduction available from: - BRE briefing papers (+ videos) are available free of charge from:

20 Thanks S. Brown Consulting Services Ltd Thanks to UBM for use of images in this presentation