﻿ 基于数据挖掘的高速船舶柴油机使用寿命预测
 舰船科学技术  2022, Vol. 44 Issue (16): 101-104    DOI: 10.3404/j.issn.1672-7649.2022.16.020 PDF

Life prediction of high-speed marine diesel engine based on data mining
WU Chao-cheng
Beihai Ennova Cruise Co.,Ltd., Beihai 536000, China
Abstract: The performance and service life of diesel engine piston directly affect the sailing conditions of high-speed ships, so this paper studies the service life prediction of high-speed Marine diesel engine based on data mining. The data mining technology of k-means clustering algorithm was used to collect the running state of high-speed Marine diesel engine and its piston. The finite element model of diesel engine piston was constructed by using the data, and the stress changes of piston couple under mechanical load and thermodynamic coupling conditions were calculated, and the boundary conditions were determined. The finite element calculation results were imported into Femfat software, and the life of diesel engine piston was predicted by Miner criterion. The prediction results were optimized by Aeran theory, and the relationship between the number of cycles under different working conditions and piston damage was determined. The test results show that the stress of diesel engine piston is mainly concentrated in the position of excessive arc of pin hole and skirt cavity under mechanical load and thermodynamic coupling condition, and the damage of diesel engine piston is the most serious and its life is the shortest under thermodynamic coupling condition.
Key words: data mining     high speed ship     diesel engine     service life     Femfat software
0 引　言

1 高速船舶柴油机使用寿命预测 1.1 基于数据发掘技术的柴油机运行状态收集

1）对于柴油机运行状态，聚类点为任意选取的n个点，表示为 ${k_1},{k_2}, \cdots ,{k_n} \in {R^n}$

2）除非实现收敛，否则始终执行以下步骤：

 ${c_i} = \arg {\kern 1pt} {\kern 1pt} \mathop {\min {{\left\| {{x_i} - {k_j}} \right\|}^2}}\limits 。$ (1)

 ${k_j} = \sum\nolimits_{i = 1}^m {\left\{ {{c_i} = j} \right\}} {x_i}/\sum\nolimits_{i = 1}^m {\left\{ {{c_i} = j} \right\}} 。$ (2)

3）为实现计算收敛性表达，构建一个畸变函数：

 $J\left( {c,k} \right) = {\sum\limits_{i = 1}^m {\left\| {{x_i} - {k_{{c_i}}}} \right\|} ^2}。$ (3)

 $\left\{ \begin{split} &{x_{ij}} = {x_{ij}} - {{\bar x}_j}/\sqrt {{var} \left( {{x_j}} \right)} ,i = 1,2, \cdots ,n;j = 1,2, \cdots ,p ，\\ &\bar x = 1/n\cdot \sum\limits_{i = 1}^n {{x_{ij}}}，\\ &{var} \left( {{x_j}} \right) = 1/\left( {n - 1} \right)\sum\limits_{i = 1}^n {{{\left( {{x_{ij}} - {{\bar x}_j}} \right)}^2}} 。\end{split} \right.\\[-50pt]$ (4)

 $G = {\lambda _i}/\sum\limits_{i = 1}^p {{\lambda _i}}。$ (5)

 ${F_i}_j = {a_{j1}}{x_{i1}} + \cdots + {a_{jp}}{x_{ip}}。$ (6)

1.2 构建高速船舶柴油机活塞有限元模型

1）模型构建

 图 1 高速船舶柴油机活塞有限元模型 Fig. 1 Finite element model of piston of high-speed marine diesel engine

2）边界条件确定

 ${h_g} = 130{D^{ - 0.2}}{q_g}^{0.8}{T_g}^{ - 0.53}{\left[ {{H_1}{H_m}\left( \begin{gathered} 1 + 2\cdot\left( {{V_O}/{V_s}} \right)\cdot \\ q_m^{ - 0.2} - 0.2 \\ \end{gathered} \right)} \right]^{0.8}} 。$ (7)

1.3 各运行状态高速船舶柴油机活塞寿命预测计算

1）机械疲劳工况下柴油机活塞寿命预测

2）热疲劳工况下柴油机活塞寿命预测

3）热机耦合疲劳工况下柴油机活塞寿命预测

Miner准则尽管预测柴油机寿命时较为有效但是并没有对荷载加载顺序加以考虑，所以利用Miner准则预测高速船舶柴油机活塞寿命后，还需要使用Aeran理论进一步分析柴油机活塞的损伤演化情况，确定循环荷载与柴油机活塞损伤之间的关系。确定高速船舶柴油机活塞寿命基本数值后使用Aeran理论计算高速船舶柴油机活塞损伤演化数值，由该值预测高速船舶柴油机活塞寿命。在有限元模型中计算活塞在不同工况下的损伤情况，利用下式计算下个应力的损伤指标：

 $\left\{ \begin{gathered} {D_i} = 1 - {\left[ {1 - \left( {{n_{\left( {i + 1} \right),eff}}/{N_{i + 1}}} \right)} \right]^{\left( {{\delta _{i + 1}}/{\mu _{i + 1}}} \right)}}, \\ {n_{\left( {i + 1} \right),eff}} = \left[ {1 - {{\left( {1 - {D_i}} \right)}^{\left( {{\delta _{i + 1}}/{\mu _{i + 1}}} \right)}}} \right]\cdot{N_{i + 1}} 。\\ \end{gathered} \right.$ (8)

 $D = \left| {{D_{i + 1}}} \right| 。$ (9)
2 试验结果分析

 图 2 高速船舶柴油机活塞应力场 Fig. 2 Piston stress field of high-speed marine diesel engine

 图 3 高速船舶柴油机活塞温度场 Fig. 3 Temperature field of piston of high-speed marine diesel engine

 图 4 热机耦合应力场 Fig. 4 Thermal-mechanical coupling stress field

3 结　语

 [1] 柯赟, 宋恩哲, 姚崇, 等. 船舶柴油机故障预测与健康管理技术综述[J]. 哈尔滨工程大学学报, 2020, 41(1): 125-131. KE Yun, SONG En-zhe, YAO Chong, et al. A review: ship diesel engine prognostics and health management technology[J]. Journal of Harbin Engineering University, 2020, 41(1): 125-131. DOI:10.11990/jheu.201903068 [2] 王浩宇, 徐建安, 曲东越. 某船用柴油机活塞件疲劳寿命预测及损伤演化分析[J]. 内燃机工程, 2020, 41(6): 86-94. WANG Hao-yu, XU Jian-an, QU Dong-yue. Fatigue life prediction and damage evolution analysis of marine diesel engine pistons[J]. Chinese Internal Combustion Engine Engineering, 2020, 41(6): 86-94. [3] 吴锐, 马洁, 丁恺林. 航空涡扇引擎剩余使用寿命预测算法研究[J]. 南京理工大学学报, 2019, 43(6): 708-714. WU Rui, MA Jie, DING Kai-lin. Research on the prediction algorithm of the remaining service life of aviation turbofan engine[J]. Journal of Nanjing University of Science and Technology, 2019, 43(6): 708-714. [4] 王常浩, 刘淑杰, 王轶凡, 等. 再制造航空发动机涡轮盘LCF寿命预测研究[J]. 大连理工大学学报, 2019, 59(4): 366-371. DOI:10.7511/dllgxb201904006 [5] 王赟, 景博, 焦晓璇, 等. 基于自适应组合核函数的RVM剩余寿命预测研究[J]. 电子测量与仪器学报, 2019, 33(6): 59-68. WANG Yun, JING Bo, JIAO Xiao-xuan, et al. Research on residual life prediction of RVM based on adaptive multi-kernel function[J]. Journal of Electronic Measurement and Instrumentation, 2019, 33(6): 59-68. DOI:10.13382/j.jemi.B1902042 [6] 郭忠义, 李永华, 李关辉, 等. 装备系统剩余使用寿命预测技术研究进展[J]. 南京航空航天大学学报, 2022, 54(3): 341-364. DOI:10.16356/j.1005-2615.2022.03.001 [7] 焦品博, 王海燕, 孙超, 等. 基于长短期记忆网络的船舶主柴油机性能预测[J]. 内燃机学报, 2021, 39(3): 250-256. JIAO Pin-bo, WANG Hai-yan, SUN Chao, et al. performance Prediction of marine main diesel engine based on long short-term memory network[J]. Transactions of Csice, 2021, 39(3): 250-256.