基于BN和ANN联合模型的舰船舱室火灾探测方法

Ship compartment fire detection method based on joint BN and ANN model

针对平时或战时火灾传感器可能出现的故障或失效问题,基于贝叶斯网络(BN)的不确定性推理特性和神经网络(ANN)良好的非线性映射能力,提出基于二者联合模型的舱室火灾探测方法,分别在正常、添加随机噪声和传感器故障条件下对模型性能进行仿真测试。结果表明,联合模型具有较强的抗干扰能力,在设定的各种情况下均能正确地判断火灾状态,具有良好的探测准确度与响应速度,单次探测耗时仅为10 ms,可有效解决舰船舱室火灾探测过程信息不确定、不完整和实时性要求高的问题。切实增强舱室火灾的早期自动探测能力。

To deal with malfunction and failure of fire sensors in peacetime and wartime, a joint model based on Bayesian Network（BN） and Artificial Neural Network（ANN） was put forward on account of BN＇s characteristic of reasoning uncertain knowledge and ANN＇s excellent nonlinear mapping ability. Joint Model was separately tested in circumstances with stochastic noises, failures of sensors and nothing abnormal, which monitors ship＇s diversified environments. Joint Model rightly estimated the fire state with acceptable error in all tested examples, and each detection took 10 ms in average. The results of simulation show that Joint Model has satisfying accuracy and responding speed, and the outstanding ability of anti-jamming, therefore, it is good at estimating fire state in circumstances with uncertain and incomplete information in real time. It can better improve early automatic detection ability to detect ship compartment fire early and automatically.