Skip to main content
SpringerLink
Log in

A Full-Scale Prototype Multisensor System for Damage Control and Situational Awareness

  • Published:
Fire Technology Aims and scope Submit manuscript

Abstract

The U.S. Naval Research Laboratory has developed a real-time, remote detection system for damage control and situational awareness, called “Volume Sensor”, as part of the Advanced Volume Sensor Task, an important element of the U.S. Navy’s Office of Naval Research, Future Naval Capabilities program, Advanced Damage Countermeasures. The objective of the Advanced Volume Sensor Task was to develop an affordable detection system that could identify shipboard damage control conditions and provide real-time threat level information for damage control events (such as flaming and smoldering fires, explosions, pipe ruptures, flooding, and gas releases) while eliminating the false alarms typical of fire detection systems in industrial environments. The approach was to build a multisensor, multicriteria system from low cost commercial-off-the-shelf hardware components integrated with intelligent software and data fusion algorithms. Two multicompartment prototype Volume Sensor systems were constructed at NRL and tested with a series of simulated damage control events at the Navy’s full-scale fire test facility, the ex-USS Shadwell in Mobile Bay, AL. Results from this test series indicate that the Volume Sensor Prototypes performed as well or better than commercial video image detection and point-detection systems in critical quality metrics for fire detection while also providing additional situational awareness for flooding scenarios, fire suppression system activations, and gas release events.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

Notes

  1. For example, a signal from the optical IR sensor with a level well above background may be observed from a flaming fire source, an overheating engine, or a person welding, corresponding respectively to flame, thermal, and nuisance events in Volume Sensor.

  2. Alternatively, a channel could be a sensor algorithm that generated values for multiple features or events, one per data block.

  3. UDP is similar to, but simpler than the more common transmission control protocol (TCP).

  4. Sensor algorithm information is typically binary valued (0––all quiet, 1––event/alarm), and for that reason is better suited to Boolean decision logic. Raw sensor data are typically real valued (0 ≤ × ≤ 1), and thus well suited for mathematical pattern recognition algorithms and statistical modeling.

  5. For example, a water alarm for a flaming trash can source is considered an inappropriate alarm, and therefore incorrect.

  6. Incorrect alarms to nuisance sources were considered false positive alarms.

  7. The number of monitoring periods (234) is equal the number of compartments (6) times the number of tests (39).

Abbreviations

ACST:

Acoustic sensor system

ADC:

Advanced Damage Countermeasures

AFSS:

Autonomic fire suppression system

CCD:

Charge coupled device

CCTV:

Closed circuit television

COTS:

Commercial-off-the-shelf

CnC:

Command and control

DCA:

Damage control assistant

DFM:

Data fusion module

DoD:

Department of Defense

DVR:

Digital video recorder

EDM:

Engineering development model

EST:

Edwards Systems Technology

ESTI:

EST ionization detector

ESTP:

EST photoelectric detector

ESTM:

EST multicriteria detector

FNC:

Future Naval Capabilities program

FOV:

Field-of-view

GUI:

Graphical user interface

IEEE:

Institute of Electrical and Electronics Engineers

IP:

Internet protocol

IPA:

Isopropyl alcohol

IR:

Infrared

LWVD:

Long wavelength video detection

NIR:

Near-infrared

NRL:

Naval Research Laboratory

PC:

Personal computer

PCA:

Principal components analysis

PVLS:

Peripheral vertical launch system

SBVS:

Spectral-based volume sensor system

SCBA:

Self-contained breathing apparatus

SCS:

Supervisory control system

SS:

Sensor suite

TCP:

Transmission control protocol

UDP:

User datagram protocol

UV:

Ultraviolet

VID:

Video image detection system

VIDF:

Fastcom Smoke and Fire Alert system

VIDA:

AxonX Signifire system

VS:

Volume Sensor

VS5:

Volume Sensor 5

VSCS:

Volume Sensor communications specification

VSP1,2:

Prototype Volume Sensor systems

XML:

Extensible markup language

References

  1. Hinkle JB, Glover TL (2004) Reduced manning in DDG 51 class warships: challenges, opportunities, and the way ahead for reduced manning on all US navy ships. Engineering the total ship (ETS) symposium. http://stinet.dtic.mil

  2. Williams FW et al (2003) DC-ARM final demonstration report, NRL/FR/6180—03-10,056. US Naval Research Laboratory. http://stinet.dtic.mil, June 23

  3. Luo RC, Yih CC, Su KL (2002) Multisensor fusion and integration: approaches, applications, and future research directions. IEEE Sens J 2(2):107–119. doi:10.1109/JSEN.2002.1000251

    Article  Google Scholar 

  4. Carhart HW, Toomey TA, Williams FW (1992) The ex-USS Shadwell full-scale fire research and test ship, NRL memorandum report 6074. US Naval Research Laboratory. http://stinet.dtic.mil. Revised 20 Jan 1988, reissued

  5. Lynch JA et al (2005) Volume sensor development test series 5––multi-compartment system, NRL/MR/6180—05-8931. US Naval Research Laboratory, http://stinet.dtic.mil, December 30

  6. Privalov G, Privalov D (2001) Early fire detection method and apparatus, United States Patent 6,184,792, February 6

  7. Rizzotti D, Schibli N, Straumann W (2005) Process and device for detecting fires based on image analysis. United States Patent 6,937,742, August 30

  8. Gottuk DT, et al. (2006) Video image fire detection for shipboard use. Fire Saf J 41(4):321–326. doi:10.1016/j.firesaf.2005.12.007

    Article  Google Scholar 

  9. Owrutsky JC et al (2003) Spectral based volume sensor component, NRL/MR/6110–03-8694. US Naval Research Laboratory. http://stinet.dtic.mil, July 30

  10. Owrutsky JC, et al. (2006) Long wavelength video detection of fire in ship compartments. Fire Saf J 41(4):315–320. doi:10.1016/j.firesaf.2005.11.011

    Article  Google Scholar 

  11. Thomas PJ (1993) Near-infrared forest-fire detection concept. Appl Opt 32(27):5348

    Article  Google Scholar 

  12. Lasaponara R et al (2003) A self adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection. Int J Remote Sens 24(8):1723–1749. doi:10.1080/01431160210144723

    Article  Google Scholar 

  13. Wieser D, Brupbacher T (2001) Smoke detection in tunnels using video images, NIST SP 965

  14. Sentenac T, Le Maolt Y, Orteu JJ (2002) Evaluation of a charge-coupled device-based video sensor for aircraft cargo surveillance. Opt Eng 41(4):796–810. doi:10.1117/1.1459450

    Article  Google Scholar 

  15. Chan WS, Burge JW (1999) Imaging flame detection system. United States Patent 5,937,077, August 10

  16. Steinhurst DA et al (2003) Long wavelength video-based event detection, preliminary results from the CVNX and VS1 test series, ex-USS Shadwell, April 7–25, 2003, NRL/MR/6110–03-8733. US Naval Research Laboratory, http://stinet.dtic.mil, December 31

  17. Wittkopp T, Hecker C, Opitz D (2001) The cargo fire monitoring system (CFMS) for the visualization of fire events in aircraft cargo holds. In: Beall K, Grosshandler W, Luck H (eds) Proceedings of AUBE 2001: 12th international conference on automatic fire detection, March 25–28

  18. Owrutsky JC, Steinhurst DA (2006) Fire detection method. United States Patent, US 7,154,400 B2, Dec. 26

  19. Zhu YJ et al (1998) Experimental and numerical evaluation of a near infrared fire detector. In: Slaughter KC (ed) Proceedings of fire research and engineering, second international conference, ICFRE2, p 512

  20. Lloyd AC et al (1998) Fire detection using reflected near infrared radiation and source temperature discrimination. NIST GCR 98:747

    Google Scholar 

  21. Sivathanu Y et al (2000) Flame and smoke detector. United States Patent 6,111,511, August 29

  22. Vdacek A et al (2002) Remote optical detection of biomass burning using a potassium emission signature. Int J Remote Sens 23(13):2721–2726. doi:10.1080/01431160110109633

    Article  Google Scholar 

  23. Fire Protection Equipment Directory, Underwriters Laboratories, Inc. Northbrook, IL, 2007

  24. Malinowski ER (1991) Factor analysis in chemistry, 2nd edn. John Wiley & Sons, New York

    MATH  Google Scholar 

  25. Steinhurst DA et al (2005) Spectral-based volume sensor testbed algorithm development, Test series VS2, NRL/MR/6110—05-8856. US Naval Research Laboratory, http://stinet.dtic.mil

  26. Kupperman DS, Claytor TN, Groenwald R (1985) Acoustic leak detection for reactor cooling systems. Nucl Eng Des 86(1):13–20. doi:10.1016/0029-5493(85)90204-3

    Article  Google Scholar 

  27. Fischer K, Preusser G (1991) Methods for leak detection for KWU pressurized and boiling water-reactors. Nucl Eng Des 128(1):43–49. doi:10.1016/0029-5493(91)90248-G

    Article  Google Scholar 

  28. Wales SC et al (2004) Acoustic event signatures for damage control: water events and shipboard ambient noise, NRL/MR/7120–04-8845. US Naval Research Laboratory. http://stinet.dtic.mil, October 12

  29. Nichols M (2001)A survey of multisensor data fusion systems. In: Hall DL, Llinas J (eds) Handbook of multisensor data fusion, CRC Press, New York, pp 22-1–22-7

    Google Scholar 

  30. Luo RC, Kay MG (1989) Multisensor integration and fusion in intelligent systems. IEEE Trans Syst Man Cybern 19(5):901–931. doi:10.1109/21.44007

    Article  Google Scholar 

  31. Klein LA (2004) Sensor and data fusion: a tool for information assessment and decision making, SPIE Press, Bellingham, WA

    Book  Google Scholar 

  32. Pfister G (1997) Multisensor/multicriteria fire detection: a new trend rapidly becomes state of the art. Fire Technol 33(2):115–139. doi:10.1023/A:1015343000494

    Article  Google Scholar 

  33. (2004) Proceedings of the 13th International Conference on Automatic Fire Detection—AUBE 2004 Duisberg, Germany, September 14–16

  34. Liu Z, Kim AK (2003) Review of recent developments in fire detection technologies. J Fire Prot Eng 13(2):129–151. doi:10.1177/1042391503013002003

    Article  Google Scholar 

  35. Luo RC, Su KL (1999) A review of high-level multisensor fusion: approaches and applications. Proceedings of the 1999 IEEE international conference on multisensor fusion and integration for intelligent systems, Taipei, Taiwan, R.O.C., August

  36. Lynch JA et al (2004) Volume sensor development test series 2––lighting conditions, camera settings, and spectral and acoustic signatures, NRL/MR/6180—04-8843. US Naval Research Laboratory. http://stinet.dtic.mil, November 24

  37. Rose-Pehrsson SL et al (2000) Multi-criteria fire detection systems using a probabilistic neural network. Sensors Actuators B 69(3):325–335. doi:10.1016/S0925-4005(00)00481-0

    Article  Google Scholar 

  38. Rose-Pehrsson SL et al (2003) Early warning fire detection system using a probabilistic neural network. Fire Technol 39(2):147–171. doi:10.1023/A:1024260130050

    Article  Google Scholar 

  39. Shaffer RE, Rose-Pehrsson SL (1999) Improved probabilistic neural network algorithm for chemical sensor array pattern recognition. Anal Chem 71(19):4263–4271. doi:10.1021/ac990238

    Article  Google Scholar 

  40. Hart SJ et al (2001) Real-time classification performance and failure mode analysis of a physical/chemical sensor array and a probabilistic neural network. Field Anal Chem Technol 5(5):244–258. doi:10.1002/fact.10004

    Article  Google Scholar 

  41. Hart SJ, Shaffer RE, Rose-Pehrsson SL (2001) Using physics-based modeler outputs to train probabilistic neural networks for unexploded ordnance (UXO) classification in magnetometry surveys. IEEE Trans Geosci Rem Sens 39(4):797–804. doi:10.1109/36.917899

    Google Scholar 

  42. Collins LM et al (2001) A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection. IEEE Trans Fuzzy Syst 9(1):17–30. doi:10.1109/91.917111

    Article  Google Scholar 

  43. Bray T et al (eds) (2004) Extensible markup language (XML) 1.1, W3C recommendation. http://www.w3.org/TR/xml11/, April 15

  44. Fairmount Automation, Inc. 4621 West Chester Pike, Newtown Square, PA 19073. Autonomic Fire Suppression System (AFSS) Engineering Development Model (EDM) http://www.fairmountautomation.com/Services/AutomatedDamageControl.htm

  45. Drake C (2007) object-oriented programming with C++ and Smalltalk. Prentice Hall, London

    Google Scholar 

  46. Lynch JA et al (2006) Volume sensor development test series 4 results––multi-component prototype evaluation, NRL/MR/6180—06-8934. US Naval Research Laboratory. http://stinet.dtic.mil, January 25

  47. Rose-Pehrsson SL et al (2006) Volume sensor for damage assessment and situational awareness. Fire Saf J 41(4):301–310. doi:10.1016/j.firesaf.2005.12.005

    Article  Google Scholar 

  48. Vibro-Meter, Inc. 10 Ammon Drive, Manchester, NH 03103. http://www.vibro-meter.com/industrial/detector.html

  49. Fastcom Technology SA Boulevard de Grancy 19A, CH-1006 Lausanne, Switzerland. http://www.fastcom.ch/

  50. axonX LLC, 2400 Boston Street, suite 326, Baltimore, MD 21224. http://www.axonx.com/

  51. Axis Communications Inc 100 Apollo Drive, Chelmsford, MA 01824. http://www.axis.com/products/cam_241/index.htm

  52. Edwards Systems Technology Inc 90 Fieldstone Court, Cheshire, CT 06410. http://www.est.net/

Download references

Acknowledgments

This work was funded by the U.S. Navy Office of Naval Research’s Future Naval Capabilities, Advanced Damage Countermeasures program. Commercial VID manufacturers, Fastcom Technology and axonX have collaborated in this research. The authors thank Mr. John Farley and Dr. Frederick Williams for their valuable assistance in this program. The crew of the ex-USS Shadwell provided much assistance in acquiring data used in the development of the prototype detection systems.

Author information

Authors and Affiliations

  1. Nova Research, Inc., Alexandria, VA, USA

    Christian P. Minor & Daniel A. Steinhurst

  2. Chemistry Division, US Naval Research Laboratory, Washington, DC, USA

    Kevin J. Johnson, Susan L. Rose-Pehrsson & Jeffrey C. Owrutsky

  3. Acoustics Division, US Naval Research Laboratory, Washington, DC, USA

    Stephen C. Wales

  4. Hughes Associates, Inc., Baltimore, MD, USA

    Daniel T. Gottuk

Authors
  1. Christian P. Minor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin J. Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Susan L. Rose-Pehrsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeffrey C. Owrutsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stephen C. Wales
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Daniel A. Steinhurst
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniel T. Gottuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susan L. Rose-Pehrsson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Minor, C.P., Johnson, K.J., Rose-Pehrsson, S.L. et al. A Full-Scale Prototype Multisensor System for Damage Control and Situational Awareness. Fire Technol 46, 437–469 (2010). https://doi.org/10.1007/s10694-009-0103-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10694-009-0103-y

Keywords

Search

Navigation