Facebook Pixel Code
x
We use cookies to create the best experience for you. Keep on browsing if you are OK with that, or find out how to manage cookies.

Reduction of the Probability of False Alarms and Shorten the Time between Fire Onset and Alarm Response in Shipboard Fires Essay Example

Show related essays

Reduction of the Probability of False Alarms and Shorten the Time between Fire Onset and Alarm Response in Shipboard Fires

Reduction of the Probability of False Alarms and Shorten the Time between Fire Onset and Alarm Response in Shipboard Fires. The grey model (GM) has been successfully and widely used (Kuo, 2001, 55). GM is often applied to prediction in time-varying non-linear systems. Kuo and Wu (2001, 59) used GM to predict the deformation of thin ship panels. Yuan et al. (2000, 13) proposed a method based on grey theory to predict gas-in-oil concentrations in an oil-filled transformer. This present paper improves upon the above studies by endowing a dual-sensor fuzzy detection system with GM predictive ability, thereby shortening alarm response time and increasing alarm accuracy. 1 presents a block diagram of the proposed system, showing a temperature sensor, a smoke sensor, individual GM circuits for each sensor and a fuzzy classification system.

The adaptive fuzzy classification system is established by the use of various fire/non-fire data configurations (readily obtained from the Taiwan National Fire Administration Ministry) and from this builds a set of fuzzy rules. Next, two grey-prediction models are set up, one for each sensor. Each GM system interprets the ongoing changes in the dynamic behavior of its respective sensor. Raw data trends, which indicate possible fire situations, i., rapid increase or a continued rise in smoke or temperature slope, result in higher GM predictive output values than the equivalent raw sensor output values. Thus, incorporation of GM between sensor and computer anticipates future sensor values, allowing the system to make fire alarm response before actual alarm conditions, without increasing susceptibility to false alarms. The algorithm in this study is modified from Nozaki, (1996, 238) and Ishibuchi, (1999, 1040). The modified algorithm is used to classify situations as fire or non-fire and consists of three procedures: (1) an automatic procedure for generation of fuzzy rules; (2) a classification procedure; (3) a fuzzy rule self-learning procedure. First, the fuzzy system is established by inputting data for various fire conditions (i. hot smokeless fire, smoky cool fire, etc.) and generating a fuzzy rule base.   . Reduction of the Probability of False Alarms and Shorten the Time between Fire Onset and Alarm Response in Shipboard Fires.

References

Chen S, Yi J, Zhao Y. Self-learning fuzzy neural network and its application to fire auto-detecting in fire protection system. Proceeding of the Third World congress on Intelligent Control and Automation, Hefei, China, June 28–July 2, 2000, pp. 244-250.

H. Ishibuchi and T. Nakashima, Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans Ind Electron 46 6 (1999), pp. 1038–1057.

H.C. Kuo and L.J. Wu, Prediction of deformation to thin ship panels for different heat sources. J Ship Prod 17 2 (2001), pp. 52–61.

J. Deng, Control problems of grey system. Systems Control Lett 5 (2002), pp. 288–294.

K. Nozaki, H. Ishibuchi and H. Tanaka, Adaptive fuzzy rule-based classification systems. IEEE Trans Fuzzy Systems 4 3 (1996), pp. 238–250.

M. Setnes and H. Roubos, GA-FUZZY modeling and classification: complexity and performance. IEEE Trans Fuzzy system 8 5 (2000), pp. 509–522.

Neubauer A. Genetic algorithms in automatic fire detection technology. Second International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, Galesia, September 2–4, 1997, pp. 9.

UL 268, Standard for smoke detectors for fire protective signaling systems, 2nd ed. Northbrook, IL: Underwriters Laboratories, 2003, pp. 3-5.

Wang X, Xiao J, Bao M. A ship fire alarm system based on fuzzy neural network. Proceeding of the Third World Congress on Intelligent Control and Automation, Hefei, China, June 28–July 2, 2000.

Yuan B, Guo J, Tang G, Wang L. Using grey theory to predict the gas-in-oil concentrations in oil-filled transformer. Proceedings of the 6th international conference on properties and applications of dielectric materials, Xi’an, China, June 21–26, 2000, pp. 10-15.

Z. Li, A. Khananian, R.H. Fraser and J. Cihlar, Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery. IEEE Trans Geosci Remote Sensing 39 9 (2001), pp. 1859–1870.

Close ✕
Tracy Smith Editor&Proofreader
Expert in: Technology, Agriculture, Information Technology
Hire an Editor
Matt Hamilton Writer
Expert in: Technology, Military, Engineering and Construction
Hire a Writer
preview essay on Reduction of the Probability of False Alarms and Shorten the Time between Fire Onset and Alarm Response in Shipboard Fires
  • Pages: 20 (5000 words)
  • Document Type: Essay
  • Subject: Technology
  • Level: Masters
WE CAN HELP TO FIND AN ESSAYDidn't find an essay?

Please type your essay title, choose your document type, enter your email and we send you essay samples