BP神经网络在远洋船舶远程监控中的应用研究

Applying back propagation neural networks to remote monitoring of ocean-going ships

为了满足公司对于远洋船舶更加有效监控的要求,应用BP神经网络对监控系统加以改进,使船舶远程监控系统发出预警信号,并能在船舶上报警.相应参数识别码第一时间到达岸上公司,岸上公司可以在最佳时间协助船舶对设备进行维修.BP神经网络在远程监控系统的应用分析过程中,以6缸柴油主机排气温度变化趋势为模型,利用BP神经网络良好的学习特性,建立了排气温度变化的持续升高预警模型及其他非预警模型.模拟结论经验证表明,模拟结果与样本之间的误差小于5%,能够准确判断故障趋势并能预报警,改进了远洋船舶的远程故障监测系统.

In order to satisfy the need for remote monitoring of ocean-going ships by more effective methods, back propagation （BP） neural networks were applied to improve existing monitoring systems by anticipating failure and so outputting warning signals instead of failure signals. During this process, the alarm apparatus on board could be actuated while relevant failure identification codes are sent to company management ashore. Subsequently, the best method to solve the problem could be worked out between the ship＇s crew and engineers ashore. To analyze the application of BP neural networks to remote surveillance, changing trends in the exhaust temperature of a six cylinder main diesel engine were simulated. Making use of the learning ability of BP neural networks, a warning model was proposed for when continually rising exhaust temperature potentially leads to engine failure. Five more non-warning models were established for other conditions. The errors between the samples and the results simulated by BP neural networks were smaller than 5% , consequently BP neural networks can judge the trend of failure and improve monitoring systems by implementing failure prediction functions.