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.