柴油机内部活塞性能与使用时长直接影响高速船舶的航行情况,提出基于数据挖掘的高速船舶柴油机使用寿命预测方法。利用k-means聚类算法数据挖掘技术收集高速船舶柴油机及其活塞的运行状态,利用该数据构建柴油机活塞的有限元模型,计算机械荷载、热力耦合等工况下活塞偶的应力变化,并确定边界条件;将有限元计算结果导入Femfat软件,利用Miner准则预测柴油机活塞寿命,结合Aeran理论优化预测计算结果,确定不同工况循环次数与采油机活塞损伤的关系。试验结果表明,机械荷载与热力耦合工况下,柴油机活塞的应力主要集中在销孔过渡圆弧以及裙中腔等位置,热力耦合工况下柴油机活塞损伤最严重,寿命也最短。
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.
2022,44(16): 101-104 收稿日期:2022-01-09
DOI:10.3404/j.issn.1672-7649.2022.16.020
分类号:TK422
作者简介:伍朝澄(1982-),男,本科,工程师,主要从事船舶修造及轮机管理
参考文献:
[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
[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
[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
[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
[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