船舶转叶式舵机转叶外端的密封件与缸体内壁长期处于高压紧密贴合状态，叶片密封件产生异常磨损，导致密封失效和舵机功能丧失，感知转叶式舵机密封件的工作状态和对其进行故障诊断极其重要。本文提出基于C#和Matlab的混合编程方法，以提高船舶转叶式舵机密封件故障诊断的准确性和效率。首先采用Matlab对转叶式舵机密封件的振动和摩擦系数信号进行特征提取；然后采用训练后的BP神经网络进行故障诊断，采用C#和Matlab混合编程的方法设计船舶转叶式舵机故障诊断系统。最后，通过案例来验证系统的可行性。结果表明，该系统能够准确识别转叶式舵机密封件的磨损状态，为保障船舶转叶式舵机可靠性运行提供了技术支撑。
The seal at the outer end of the rotor blade of the ship's rotary vane steering gear and the inner wall of the cylinder are in a state of high pressure and close contact for a long time, which causes abnormal wear of the blade seal and results in seal failure and loss of function of the rotary vane steering gear. In order to ensure the safety of the ship's navigation, it is extremely important to perceive the working status of the rotary vane steering gear seal and to diagnose its faults. In this paper, a hybrid programming method based on C# and Matlab is proposed to improve the accuracy and efficiency of fault diagnosis of ship rotary vane steering gear seals. Firstly, Matlab is used to extract features of vibration and friction coefficient signals of rotary vane steering gear seals. The trained BP neural network is used for fault diagnosis. The fault diagnosis system of the ship's rotary vane steering gear is designed in the way of mixed programming. Finally, the diagnosis accuracy of the diagnosis system is verified by the fault case. The experimental results show that the system can quickly and accurately identify the wear state of the rotary vane steering gear seal, which provides technical support for ensuring the reliable operation of the ship's rotary vane steering gear.
2023,45(20): 146-150 收稿日期：2022-9-27
DOI：10.3404/j.issn.1672-7649.2023.20.027
分类号：U664.4
基金项目：工信部高技术船舶科研项目（工信部装函[2019]358号）
作者简介：王攀(1998-),男,硕士研究生,研究方向为船舶机械故障诊断
参考文献：
[1] 王必改. 船舶舵机故障的原因分析与探讨[J]. 天津航海, 2020(4): 16–19
[2] 张李伟, 庄才亮, 夏明宽, 等. 性能卓越的转叶式舵机设计[J]. 船舶与海洋工程, 2014(2): 35–39
[3] 徐恒达. 船舶柴油机故障分析诊断系统[D]. 大连: 大连理工大学, 2015.
[4] 胡良谋, 曹克强, 徐浩军. 基于回归型支持向量机的液压舵机故障诊断[J]. 系统仿真学报, 2007(23): 5509–5512
[5] 章家保, 徐伟. 模糊聚类方法在电动舵机致命故障检测中的应用[J]. 红外与激光工程, 2014, 43(S1): 123–129
[6] XU Y X, YANG C N. Fault recognition of electric servo steering gear based on long and short-term memory neural network[C]//International Conference on Unmanned Aircraft Systems (ICUAS). Athens, Greece: IEEE, 2020: 1605–1613.
[7] MARTINS M R, da Silva D F, MARUYAMA F M, et al. The bayesian networks applied to a steering gear system fault diagnostics[C]//International Conference on Offshore Mechanics and Arctic Engineering. Shanghai, China: ASME, 2010: 639–645.
[8] ZHANG Y, AN J P, CHEN P. Research of hybrid programming with c#. net and matlab[J]. Physics Procedia, 2012, 24: 1677–1681
[9] 刘鑫, 贾云献, 孙磊, 等. 基于BP神经网络的变速箱故障诊断方法研究[J]. 计算机测量与控制, 2017, 25(1): 12–15
[10] MA L, SHI H, ZHANG H, et al. High-sensitivity distinguishing and detection method for wear debris in oil of marine machinery[J]. Ocean Engineering, 2020, 215: 107452
[11] 向晶晶, 董从林, 白秀琴, 等. 转叶式舵机密封件在不同磨粒尺寸下的摩擦学特征研究[J]. 中国机械工程, 2022, 33(4): 397–405
[12] 张祥罗. 风力机中发电机在线故障特征提取与故障诊断系统研究[D]. 广州: 华南理工大学, 2013.
[13] 侯伟真, 李正强, 张玉环, 等. 基于C#和Matlab的HJ-1-CCD气溶胶光学厚度反演系统的开发[J]. 遥感信息, 2013, 28(5): 28–31+36
[14] 刘亚, 王静, 田新诚. 基于C#和Matlab混合编程的轴承故障诊断系统[J]. 计算机应用, 2018, 38(S2): 236–238+242
