作为船舶系统重要的组成部分，柴油机工作状态与船舶安全航行息息相关。因此，对船舶柴油机故障诊断方法的研究具有重要的现实意义。以SOM神经网络和BP神经网络为理论基础，将二者融合构建SOM-BP神经网络，用于船舶柴油机故障诊断。通过仿真试验，验证了SOM-BP神经网络在船舶柴油机故障诊断中的有效性。
The working status of diesel engine which is the important part of the entire ship system is directly related to the safe navigation of the ship. So the study on marine diesel engine fault diagnosis method is particularly important. The SOM-BP network model based on the principal of self-organization network and back pressure network was established to diagnose marine diesel engine fault. Effectiveness of the SOM-BP neural network for marine diesel engine fault diagnosis are verified through simulation experiment.
2023,45(22): 121-125 收稿日期：2022-12-7
DOI：10.3404/j.issn.1672-7649.2023.22.022
分类号：U665.26
作者简介：李根(1994-),男,硕士,助理工程师,研究方向为复杂系统故障诊断
参考文献：
[1] 吴瑞莉. 船舶柴油机机械磨损故障诊断方法[J]. 舰船科学技术, 2018, 40(7): 109–111
[2] 陈颖. 自适应RBF神经网络在船舶柴油机故障诊断系统中的应用[J]. 舰船科学技术, 2021, 43(11): 85–87
[3] 罗亮, 李国璋, 杨权, 等. SOM-BP神经网络在机械故障诊断中的应用研究[J]. 车用发动机, 2008, 2(174): 72–75
[4] JI Mengran, CHEN Gang, YANG Qing, et al. Fault diagnosis of analog circuit based on PSO and BP neural network[J]. Journal of Shenyang Ligong University, 2014, 33(5): 90–94
[5] SUN Y J, ZHANG S, MIAO C X, et al. Improved BP neural network for transformer fault diagnosis[J], Journal of China University of Mining and Technology, 2007, 17(1): 138-142.
[6] 余锋, 许开熙, 黄俊, 等. 基于 BP 神经网络的高压断路器故障诊断[J]. 真空科学与技术学报, 2019, 39(1): 249–253
[7] 陈时游, 陆海, 张少泉, 等. SOM 神经网络对永磁同步电机的故障诊断[J]. 软件, 2018, 39(8): 70–73
[8] 黄金波, 杨萍. 基于SOM神经网络的矿井提升机故障诊断技术研究[J]. 设备监理, 2019, 7: 53–54
[9] 刘兆炜, 王汉军, 李丹, 等. 改进SOM神经网络在电力调度故障诊断中的应用[J]. 计算机系统应用, 2018, 27(3): 179–185
[10] CHEN Xin-yi, YAN Xue-feng. Using improved self-organizing map for fault diagnosis in chemical industry process[J]. Chemical Engineering Research and Design, 2012, 90(12): 2262–2277
[11] 赵文均. 基于SOM和BP网络的K均值聚类算法分析[J]. 电脑知识与技术, 2020, 16(9): 24–26
