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摘要: 舱室是船舶的重要组成部分。当某舱室发生火灾时，其相邻舱室极易引发连锁性起火，此时如果报警系统不能及时、准确、可靠地给出各舱室的关联报警信息，就会对整个船舶造成严重危害。为了增加船舶火灾报警系统的早期预警与关联报警功能，分析了通常情况下典型船舶舱室的火灾报警判别要素并对其进行量化，建立舱室火灾报警优先级BP神经网络评估模型，运用LM算法对该评估模型进行学习训练，并通过测试样本验证该船舶舱室火灾报警优先级评估模型的可行性与准确性。该方法有助于提高报警系统对各舱室火灾探测报警的准确性，从而可降低由于舱室关联起火而导致发生船舶重大损失的概率。Abstract: When fire breaks out in a ship cabin， the adjacent cabins may also suffer from chain effects and catch fire. In that case， if the fire alarm system does not provide accurate and reliable information concerning the afflicted cabins timely， the entire ship could be seriously damaged. In order to improve the early warning and alert correlation function of the ship fire alarm system， this paper analyzes the diagnostic features of typical cabin fires quantitatively and establishes a BP neural network evaluation model regarding the fire alarm priority. The model is then trained via the LM algorithm. In order to validate the proposed evaluation model， several testing samples have been employed. The results show that the method significantly improves the accuracy of the fire alarm system and lowers the chance of catastrophic losses due to cabin chain fires.
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Keywords:
- cabin /
- fire /
- diagnostic feature /
- priority /
- neural network /
- BP algorithm
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