Early warning of ship fires using Bayesian probability estimation model

Economic pressure to reduce the cost of the U.S. Navy ships has brought into the focus the need to significantly reduce the size of a ship's crew. In order for an automated system to replace humans while making critical decisions, it is required that such a system be able to accurately predict future events. This paper presents a wavelet theory based prediction system to predict the occurrences of ship's fires. Furthermore, while the prediction model predicts the future events, the accuracy of prediction has to be quantified by formulating a probability index that would mirror the confidence on the prediction. As such, a Bayesian theory based probability estimation model (BPEM) is developed for estimating the probability that the predicted values are within specified limits of tolerance. Tests with the U.S Naval Research Laboratory (NRL) data, covering various fire scenarios, validate that the proposed methodology consistently provides earlier detection as compared to the published results from the INRL' early warning fire detection system (EWFD) system.

[1]  Timothy F. Cootes,et al.  Bayesian and non-Bayesian probabilistic models for medical image analysis , 2003, Image Vis. Comput..

[2]  I. Daubechies.  Ten Lectures on Wavelets , 1992 .

[3]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[4]  Dale E. Seborg,et al.  Predictive monitoring for abnormal situation management , 2001 .

[5]  S. James Press,et al.  Subjective and objective Bayesian statistics : principles, models, and applications , 2003 .

[6]  Patricia A. Tatem,et al.  DC-ARM Final Demonstration Report , 2003 .

[7]  H Putter,et al.  A Bayesian approach to parameter estimation in HIV dynamical models , 2002, Statistics in medicine.

[8]  Babu Joseph,et al.  Process Signal Features Analysis , 1994 .

[9]  R. H. Myers,et al.  Probability and Statistics for Engineers and Scientists , 1978 .

[10]  B. Bakshi,et al.  Bayesian principal component analysis , 2002 .

[11]  Makoto Akama,et al.  Bayesian analysis for the results of fatigue test using full-scale models to obtain the accurate failure probabilities of the Shinkansen vehicle axle , 2002, Reliab. Eng. Syst. Saf..

[12]  Frederick W. Williams,et al.  Development of an Early Warning Multi-criteria Fire Detection System: Analysis of Transient Fire Signatures Using a Probabilistic Neural Network. , 2000 .

[13]  R. Buizza,et al.  Using weather ensemble predictions in electricity demand forecasting , 2003 .

[14]  Frederick W. Williams,et al.  Multi-criteria fire detection systems using a probabilistic neural network , 2000 .

[15]  Bhavik Ramesh Bakshi.  Multi-resolution methods for modeling, analysis and control of chemical process operations , 1992 .