﻿ AR模型在船舶旋转机械故障诊断和状态预测技术的应用
 舰船科学技术  2022, Vol. 44 Issue (24): 177-180    DOI: 10.3404/j.issn.1672-7649.2022.24.038 PDF
AR模型在船舶旋转机械故障诊断和状态预测技术的应用

1. 河南理工大学 鹤壁工程技术学院，河南 鹤壁 458030;
2. 鹤壁天淇汽车模具有限公司，河南 鹤壁 458030

Application of AR model in fault diagnosis and condition prediction of ship rotating machinery
REN Yan1, SHI Bing-xin2
1. Hebi Institute of Engineering and Technology, Henan Polytechnic University, Hebi 458030, China;
2. Hebi Tianqi Automobile Mould Co. Ltd., Hebi 458030, China
Abstract: Ship rotating machinery, such as gears and bearings, is the key component of ship power system. Its safety and reliability directly determine the service life of the ship. Generally, the faults of ship rotating machinery are closely related to its vibration characteristics. By monitoring the vibration frequency signal of rotating machinery, the corresponding fault types can be analyzed and matched. This paper first introduces the working principle and characteristic frequency of the gear and bearing of the ship power system. Combined with the AR model of the time system, a fault diagnosis and state prediction system for ship rotating machinery is built. By analyzing the time series signals of rotating machinery components, the fault and working state of rotating machinery components are analyzed and predicted.
Key words: AR model     gear     bearing     fault diagnosis     state prediction
0 引　言

1 船舶旋转机械部件齿轮的故障特性

 图 1 渐开线齿轮啮合的示意图 Fig. 1 Schematic diagram of involute gear engagement

 $M\ddot x + C\dot x + k\left( t \right)x = F\left( t \right)。$

 $M = \frac{{{m_1}\cdot {m_2}}}{{{m_1} + {m_2}}} \text{，}$

 ${f_z} = \frac{N}{{60}}Z \text{，}$

 ${f_0} = \frac{N}{{60}}Z \text{，}$

 ${X_c}(t) = \sum\limits_{m = 0}^M {{A_m}} \cos \left( {2{\text{π}} m{f_z} + {\varphi _m}} \right) \text{，}$

2 船舶旋转机械部件轴承的故障特性

 图 2 船舶发动机滚动轴承的结构示意图 Fig. 2 Structural diagram of marine engine rolling bearing

1）胶合失效

2）腐蚀失效

3）疲劳剥落

4）塑性变形

 图 3 船舶轴承的电腐蚀、表面剥落、塑性变形等故障图 Fig. 3 Example of electric corrosion, surface peeling, plastic deformation and other failures of ship bearings

 ${f_{{n_{}}}} = 0.212\frac{{Eg}}{{R\gamma }}。$

 ${f_n} = \frac{{n\left( {{n^2} - 1} \right)}}{{2{\text{π}} {{(D/2)}^2}\sqrt {{n^2} + 1} }}\sqrt {\frac{{EIg}}{{\gamma A}}}。$

 图 4 典型船舶轴承故障振动频率信号示意图 Fig. 4 Schematic diagram of vibration frequency signal of typical ship bearing fault
3 基于AR模型的船舶旋转机械故障诊断和状态预测 3.1 时间序列AR模型原理

 ${x_k} = \sum\limits_{k = 1}^m {{\phi _i}} {x_{k - 1}} + {a_k} \text{，}$

 ${a_k} = {\phi _1}{x_{k - 1}} - {\phi _2}{x_{k - 2}} = \cdots \cdot {\phi _m}{x_{k - m}} \text{，}$

 $\delta _a^2 = E\left( {a_k^2} \right) = \frac{1}{N}\sum\limits_{k = 1}^N E \left( {{x_k} - {\phi _1}{x_{k - 1}} \cdots \cdots - {\phi _m}{x_{k - 1}}} \right.) 。$
3.2 基于AR模型的旋转机械故障诊断和状态预测

 图 5 船舶旋转机械故障诊断与预测系统原理图 Fig. 5 Schematic diagram of fault diagnosis and prediction system for marine rotating machinery

 图 6 船舶轴承实际振动加速度与AR预测加速度的对比曲线 Fig. 6 Comparison curve between actual vibration acceleration of ship bearing and AR predicted acceleration
4 结　语

 [1] 赵丹枫. 基于AR_TSM的时间序列motif关联规则挖掘方法研究[J]. 计算机应用研究, 2021, 38(2): 403-408. ZHAO Dan-feng. Research on mining method of time series motif association rules based on AR_TSM[J]. Computer Application Research, 2021, 38(2): 403-408. [2] 袁兴明. 基于线性分析及AR模型在GNSS时间序列中的应用[J]. 测绘与空间地理信息, 2020, 43(2): 63-66+72. YUAN Xing-ming. Application of linear analysis and AR model in GNSS time series[J]. Mapping and Spatial Geographic Information, 2020, 43(2): 63-66+72. DOI:10.3969/j.issn.1672-5867.2020.02.019 [3] 许小芬. 基于AR(p)型高阶模糊时间序列的磨削颤振预测方法[J]. 成组技术与生产现代化, 2018, 35(3): 50-54. XU Xiao-fen. A grinding chatter prediction method based on AR (p) high-order fuzzy time series[J]. Group Technology and Production Modernization, 2018, 35(3): 50-54. DOI:10.3969/j.issn.1006-3269.2018.03.010