- 无标题文档
查看论文信息

中文题名:

 

基于摩擦学特性的柴油机主轴承磨损热电监测方法研究

    

姓名:

 万标    

学号:

 104971140218    

保密级别:

 公开    

论文语种:

 chi    

学科代码:

 082402    

学科名称:

 工学 - 船舶与海洋工程 - 轮机工程    

学生类型:

 博士    

学位:

 工学博士    

学校:

 武汉理工大学    

院系:

 能源与动力工程学院    

专业:

 轮机工程    

研究方向:

 柴油机监测诊断    

第一导师姓名:

 杨建国    

第一导师院系:

 武汉理工大学    

完成日期:

 2021-10-14    

答辩日期:

 2021-12-01    

中文关键词:

 

主轴承 ; 磨损监测 ; 热电势 ; 船用柴油机

    

中文摘要:

船用柴油机是船舶的动力心脏,占船舶动力系统市场份额的82%以上。随着船舶柴油机强载度的提高,爆压时刻曲轴—主轴瓦摩擦副的接触比压大幅上升,严重加大了曲轴—主轴瓦润滑油膜建立的难度,使得主轴承工作条件恶劣,易产生主轴瓦的过度摩擦磨损,导致曲轴挠曲变形和抱熔折断等,严重时有可能发生曲轴箱油雾浓度过高或产生曲柄箱爆炸等恶劣故障,从而产生巨大的经济损失。因此,开展基于摩擦学特性的柴油机主轴承磨损热电监测方法研究具有十分重要的理论意义和工程实用价值。

本文分析了国内外摩擦磨损研究、主轴承润滑特性研究、智能诊断技术以及船用柴油机主轴承磨损在线监测技术现状;在滑动轴承疲劳磨损试验台对铝合金和铜合金轴承进行润滑特性和疲劳磨损试验,得出了热电—磨损量的关系;进行了4120SG型直列式柴油机试验研究,验证了主轴承磨损热电信号特征提取和故障定位方法的有效性,搭建了基于热电法的智能诊断模型,提高了故障识别率和泛化性;提出了基于热弹流理论和热电效应的船用柴油机主轴承热电势的计算方法。论文的主要研究工作如下:

(1)根据热电效应的基本原理,从两种不同材料导体组成的闭合回路中热电势的机理出发,分析了闭合导体回路热电势的产生及影响因素。结合柴油机主轴承结构特点和受力情况,分析了基于热电法的主轴承磨损监测方案的可行性;搭建了基于热电法的磨损测量系统试验台,在试验台上完成了不同转速和不同档位的干摩擦磨损试验,验证基于热电法的磨损测量系统的可测性及定位性。

(2)梳理阐述了流体动压润滑和摩擦磨损基本理论,在一定条件下推导了雷诺方程,并考虑曲轴—轴瓦微凸峰接触,建立了滑动轴承疲劳磨损试验台的多体动力学模型,对不同参数进行了正交分析,得出了轴承载荷、间隙以及粗糙度对滑动轴承疲劳磨损试验台摩擦功耗影响较强的结论。

    (3)在滑动轴承疲劳磨损试验台上对铝合金轴瓦和铜合金轴瓦进行了不同载荷与不同进油温度的润滑特性试验,得出了不同润滑条件下铝合金轴瓦和铜合金轴瓦温度、扭矩、最小油膜厚度的特性曲线;以润滑特性试验得出的边界润滑点为疲劳磨损试验工况,进行了20h的疲劳磨损试验,分析了不同材料对轴承热电特性的影响,得出了热电信号比瓦背温度和扭矩信号对轴瓦磨损更为敏感的结论,建立了热电—磨损量的关系。

(4)针对4120SG型直列式柴油机计算了不同工况下主轴承热电势,并开展了不同间隙、不同粗糙度和不同档主轴瓦磨损的故障模拟试验,试验结果表明:1)随柴油机负荷的增加,各档主轴承热电特征值增加;2)当某档主轴承磨损时,其对应的气缸发火时刻热电峰值增加,且对应的热电特征值最大;3)某档主轴承磨损程度越大,其热电特征值越大;4)通过主轴承载荷峰值对应的曲柄转角作为热电特征值角度可定位主轴承磨损的档位;5)采用决策树、随机森林、支持向量机、XGBOOST算法和KNN算法等5种监督学习算法对热电数据集进行了对比分析,结果表明其中KNN算法和SVM算法泛化能力较好,对新样本识别的准确率高达98%;

(5)考虑到直列式与V型柴油机结构的区别,采用理论与试验相结合的办法,从模态缩减基本原理和多体动力学出发,建立了TBD234V型柴油机曲轴—轴系热弹性流体多体动力学模型,并通过瞬时转速验证了模型精度,提出了基于热电效应的船用柴油机主轴承热电势的计算方法,得出了柴油机负荷、轴承间隙和轴瓦粗糙度对热电势的影响,分析了直列式和V型柴油机热电势的区别。

参考文献:

[1] Main Engine damage 2018 The Swedish Club https://www.swedishclub.com 2020.

[2] 重载往复摩擦磨损试验仪[J].摩擦学学报,2020,40(05):696.

[3] Grandin M, Wiklund U. Friction, wear and tribofilm formation on electrical contact materials in reciprocating sliding against silver-graphite[J]. Wear, 2013, 302(1-2): 1481-1491.

[4] Dohda K, Wang Z. Effects of average lubricant velocity andsliding velocity on friction behavior in mild steel sheet forming[J]. ASME Journal of Tribology, 1998, 120( 4) : 724-728.

[5] Menezes P L, Kishore, Kailas S V. Effect of surface roughness parameters and surface texture on friction and transfer layer formation in tin–steel tribo-system[J]. Journal of Materials Processing Tech, 2008, 208(1):372-382.

[6] Kirkhorn L, Frogner K, Andersson M, et al. Improved Tribotesting for Sheet Metal Forming[J]. Procedia Cirp, 2012, 3(1):507-512.

[7] Payen G R, Felder E, Repoux M, et al. Influence of contact pressure and boundary films on the frictional behaviour and on the roughness changes of galvanized steel sheets[J]. Wear, 2012, 276: 48-52.

[8] 王安宇, 莫继良, 盖小红,等. 表面粗糙度对摩擦尖叫噪声特性的影响[J]. 摩擦学学报, 2014, 34(4):400-407.

[9] 张永振, 朱均, 刘维民,等. 滑动干摩擦条件下铸铁的摩擦学特性研究[J]. 摩擦学学报, 2002, 22(2):99-102.

[10] 杨本杰, 刘小君, 董磊,等. 表面形貌对滑动接触界面摩擦行为的影响[J]. 摩擦学学报, 2014, 34(5):553-560.

[11] 马晨波, 朱华, 张文谦,等. 往复条件下织构表面的摩擦学性能研究[J]. 摩擦学学报, 2011, 31(1):50-55.

[12] 郎平,徐阳阳,袁晓帅,徐久军,单英春,韩晓光.激光参数对硼磷合金铸铁缸套微坑形貌影响研究[J].应用激光,2020,40(03):476-482.

[13] 陈文滨,马思齐,王正,徐久军.珩磨角和粗糙度对CuNiCr气缸套摩擦学性能的影响[J].车用发动机,2019(03):19-25.

[14] 李承娣,金梅,徐久军,沈岩.激光光整对高硅铝合金缸套材料摩擦磨损性能的影响[J].中国表面工程, 2017,30(05):89-94.

[15] 朱峰,徐久军,孙健,沈岩,韩晓光.松孔镀铬缸套磨损机理研究[J].内燃机学报, 2017, 35(03): 274-279.

[16] 李维山. 活塞环-气缸套摩擦磨损试验机的研究[D]. 广州:华南理工大学,2011.

[17] 徐双满. 往复式摩擦磨损试验机的研制[J]. 机车车辆工艺,1997,6(6):22-24.

[18] 张文通,马超,胡琳芳,何伟,於迪.润滑油进水对轴承磨损影响的试验研究[J].电力设备管理,2019(09):52-54

[19] 付杨杨. 径向滑动轴承综合性能试验台的研制[D].合肥工业大学,2019

[20] Greenwood J A, Williamson J B P. Contact of nominally flat surfaces[J]. Proceedings of the royal society of London. Series A. Mathematical and physical sciences, 1966, 295(1442): 300-319.

[21] Greenwood J A, Tripp J H. The contact of two nominally flat rough surfaces[J]. Proceedings of the institution of mechanical engineers, 1970, 185(1): 625-633.

[22] Chang W R, Etsion I, Bogy D B. An elastic-plastic model for the contact of rough surfaces[J]. 1987.

[23] Zhao Y, Maietta D M, Chang L. An asperity microcontact model incorporating the transition from elastic deformation to fully plastic flow[J]. J. Trib., 2000, 122(1): 86-93.

[24] Caika V, Bukovnik S, Offner G, et al. Elasto-hydrodynamic journal bearing model with pressure, temperature and shear rate dependent viscosity[C]//AITC-AIT international conference on tribology, Parma, Italy. 2006.

[25] Tao W, Yuan Y, Resh W F, et al. CFD Simulation of Connecting Rod Bearing Lubrication[R]. SAE Technical Paper, 2003.

[26] Paranjpe R S, Tseregounis S I, Viola M B. Comparison between theoretical calculations and oil film thickness measurements using the total capacitance method for crankshaft bearings in a firing engine[J]. Tribology transactions, 2000, 43(3): 345-356.

[27] Masjedi M, Khonsari M M. Film thickness and asperity load formulas for line-contact elastohydrodynamic lubrication with provision for surface roughness[J]. 2012.

[28] Kaneta M, Sakai T, Nishikawa H. Effects of surface roughness on point contact EHL[J]. Tribology Transactions, 1993, 36(4): 605-612.

[29] He Z, Zhang J, Zhang G, et al. Crankshaft-bearing evolution indexes investigation and asperity contact identification based on neural network[J]. Applied Mathematical Modelling, 2014, 38(2): 506-523.

[30] 蔡林. 流体动压滑动轴承静特性研究[D]. 哈尔滨工程大学, 2012.

[31] 王刚志. 内燃机主轴承热弹性流体动力润滑数值分析及试验研究[D]. 天津大学, 2007.

[32] 林建辉,王炳轩,魏立队.整机体下船舶柴油机主轴承热弹流体动力混合润滑研究[J].大连海事大学学报,2020,46(04):102-111.

[33] 章朝栋,赵俊生,朱桂香,李涵,李正文.曲轴平衡率对主轴承润滑特性的影响[J].内燃机学报,2020,38(02):178-184.

[34] 张亮,卢熙群,赵滨,钟宁,黄福战,刘冲培.计入曲轴及机体变形的主轴承动力学摩擦学耦合分析[J].船舶工程,2019,41(S1):146-149.

[35] 李彪,孙军,朱少禹,刘广胜,苗恩铭,李云强,朱桂香.计及曲轴轴向运动的内燃机主轴承润滑分析[J].机械工程学报,2019,55(17):94-101.

[36] AVL-List GmbH. AVL-EXCITE Theory; Version 2016; AVL-List GmbH: Graz, Austria, 2016.

[37] 王泽潇,张为乐.浅谈BP神经网络在滚动轴承故障诊断中的原理及应用[J].中国设备工程,2021(17):51-52.

[38] 李涛,段礼祥,张东宁,赵赏鑫,黄辉,毕彩霞,袁壮.自适应卷积神经网络在旋转机械故障诊断中的应用[J].振动与冲击,2020,39(16):275-282+288.

[39] 刘奇. 基于最大重叠离散小波包变换的齿轮箱故障特征提取方法研究[D].河南理工大学,2020.

[40] Zhao Z, Xu Q, Jia M. Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis[J]. Neural Computing and Applications, 2016, 27(2): 375-385.

[41] GS V, Pai S P, Sriram N S, et al. Radial basis function neural network based comparison of dimensionality reduction techniques for effective bearing diagnostics[J]. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 2013, 227(6): 640-653.

[42] Wu L, Yao B, Peng Z, et al. Fault Diagnosis of roller bearings based on a wavelet neural network and manifold learning[J]. Applied Sciences, 2017, 7(2): 158.

[43] 陈铁华,陈启卷.模糊聚类分析在水电机组振动故障诊断中的应用[J].中国电机工程学报,2002(03):44-48.

[44] Wentao S, Changhou L, Dan Z. Bearing fault diagnosis based on feature weighted FCM cluster analysis[C]//2008 international conference on computer science and software engineering. IEEE, 2008, 5: 518-521.

[45] Ting-ting X, Yan Z, Zong M, et al. A fault diagnosis method of rolling bearing based on VMD Tsallis entropy and FCM clustering[J]. Multimedia Tools and Applications, 2020, 79(39): 30069-30085.

[46] Lotfan S, Salehpour N, Adiban H, et al. Bearing fault detection using fuzzy C-means and hybrid C-means-subtractive algorithms[C]//2015 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, 2015: 1-7.

[47] Truong H, Giao P , Unsupervised Detection Of Anomalous Sound For Machine Condition Monitoring Using Different Auto-encoder Methods[C]//In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop, June 2020, pp.209–212.

[48] JI SH B, CHEN CH, JIAN F CH. Feature based fusion system for anomalous sounds monitoring[C]//In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop, June 2020.

[49] KOIZUMI Y, KAWAGUCHI Y, IMOTO K, et al, Description and discussion on DCASE2020 challenge task2: Unsupervised anormal sound detection for machine condition monitoring[C]//In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop,June 2020.

[50] SPYROS G, PRAVEER S, NIKOS K. Unsupervised representation learning by predicting imagerotations[C]//International Conference on Learning Representations, 2018.

[51] 张红, 李柱国, 陈兆能. 舰船柴油机磨损趋势预测的灰色模型方法[J]. 内燃机学报,2002, 04:362-364.

[52] 孙国亮, 曹新玉, 韩文玉.油液分析在船用调距桨液压油乳化故障中的应用[J].化学工程师, 2017, 31(03):79-81.

[53] 薛云伟, 王铁, 王道勇. 探究不同黏度的润滑油对齿轮疲劳寿命的影响[J].机械传动, 2017, 41(12):7-9+68.

[54] 姜旭峰,宗营,阮少军.基于滑油监测信息的轴承试验磨损故障预警方法研究[J].机床与液压,2020,48(22):186-191.

[55] 张海涛.基于在线油液监测的船舶发动机故障分析[J].机械设计与制造工程,2020,49(03):113-116.

[56] 李志祥,刘连伟,张冲林.油液分析技术在水轮机组故障诊断中的应用[J].水电站机电技术,2019,42(07):67-70.

[57] 都玉辉. 基于油液分析的齿轮磨损状态识别及故障预测[D].太原理工大学,2018.

[58] Zhou W, Habetler T G, Harley R G. Stator current-based bearing fault detection techniques: A general review[C]//2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. IEEE, 2007: 7-10.

[59] 杨益飞. 高速电主轴主动磁轴承温度场参数设计及控制研究[D]. 江苏大学, 2013.

[60] 徐刚. 基于光纤传感的机械设备动态监测关键技术研究与应用[D]. 武汉理工大学, 2013

[61] 薛清华. 高精度多通道温度测量技术研究[D]. 华中科技大学, 2007.

[62] 吴晨霞. 基于ZigBee技术的轴承温度监控系统[D]. 大连理工大学, 2009.

[63] 付跃飞. 基于自然热电偶法的数控铣削温度测量系统[D]. 大连理工大学, 2013.

[64] 孙鹏. 基于无线传感网的轴承温度监测系统设计[D]. 南京信息工程大学, 2014.

[65] 曹立军. 分布式光纤温度测量及数据处理技术研究[D]. 合肥工业大学, 2006.

[66] 杨汉瑞,郭宜昌,徐士博,张勇杰,房海瑞.石英晶体热特性及光纤电压传感器热致系统误差分析[J/OL].中国电机工程学报:1-10[2021-03-04].

[67] 张煜熔,廖秋雨,董昭达,张克非.光纤布拉格光栅菱形结构应变传感器[J/OL].激光技术:1-13[2021-03-04]

[68] 庞福滨,袁宇波,戴锋,赵茗.基于琼斯矩阵的反射型光纤电流传感器误差分析[J/OL].高电压技术:1-8[2021-12-03].

[69] 刘思晨,黄怿,邓传鲁,胡程勇,黄彩红,董艳华,张小贝,王廷云.基于磁致折变效应的掺铒光纤磁场传感器温度特性研究[J/OL].中国激光:1-14[2021-12-03].

[70] 张晶,吴艳茹,杨海波.考虑振动和温度影响的光纤传感器设计研究[J].激光杂志,2021,42(09):150-154.

[71] 彭岳仑. 基于光纤光栅传感的分布式轴承温度测量技术研究[D].武汉理工大学,2012.

[72] 陈金海,张文远,罗梦婷,王恒.智能滚动轴承内圈温度监测系统研究[J].仪表技术与传感器,2021(10):103-108.

[73] 黄仕勇,肖金生,龚齐清等.内燃机滑动轴承温度监测技术研究 [J]. 武汉理工大学学报(交通科学与工程版),2001年04期.

[74] Kongsberg Engine Monitoring Systems [EB/OL].https://www.kongsberg.com /maritime /products/engines-engine-room-and-automation-systems/ 2021.

[75] Glavatskih S B. A method of temperature monitoring in fluid film bearings[J]. Tribology International, 2004, 37(2): 143-148.

[76] 刘宇.船舶柴油机曲轴箱油雾浓度检测系统设计[J].机电设备,2021,38(04):67-70+74.DOI:10.16443/j.cnki.31-1420.2021.04.014.

[77] 杨鹏. 曲轴箱油雾探测装置设计研究[D].上海交通大学,2016.

[78] 郭旦,张权,孙晓蔚.柴油机曲轴箱油雾探测系统分析[J].机电设备,2015,32(04):25-29.

[79] 李红飞. 船舶柴油机曲轴箱油雾浓度检测装置的研究与设计[D].武汉理工大学,2013.

[80] 刘玉娟. 油雾浓度探测报警装置研究与设计[D].武汉理工大学,2012.

[81] 宋子平. 曲轴箱油雾探测器的设计与研究[D].哈尔滨工程大学,2012.

[82] 杜新文. 基于DSP的船舶柴油机曲轴箱油雾浓度探测器研究[D].武汉理工大学,2011.

[83] 张娟,梁煦,武茂聪,李硕.曲轴箱油雾浓度探测器的设计[J].仪表技术与传感器,2018(09):119-121+126

[84] Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Non-rotating Parts-Part 1: General Guidelines,ISO 10816-1:1995(E), 1995.

[85] Mechanical Vibration-Evaluation of Machine Vibration by Measurements on Non-rotating Parts-Part 3: General Guidelines,ISO 10816-3:1998(E), 1998.

[86] Yu Y, Junsheng C. A roller bearing fault diagnosis method based on EMD energy entropy and ANN[J]. Journal of sound and vibration, 2006, 294(1-2): 269-277.

[87] Mohanty S, Gupta K K, Raju K S. Comparative study between VMD and EMD in bearing fault diagnosis[C]//2014 9th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2014: 1-6.

[88] Mohanty S, Gupta K K, Raju K S. Hurst based vibro-acoustic feature extraction of bearing using EMD and VMD[J]. Measurement, 2018, 117: 200-220.

[89] Liu Z, Cao H, Chen X, et al. Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings[J]. Neurocomputing, 2013, 99: 399-410.

[90] Djebala A, Ouelaa N, Hamzaoui N. Detection of rolling bearing defects using discrete wavelet analysis[J]. Meccanica, 2008, 43(3): 339-348.

[91] Purushotham V, Narayanan S, Prasad S A N. Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition[J]. Ndt & E International, 2005, 38(8): 654-664.

[92] Sadegh H, Mehdi A N, Mehdi A. Classification of acoustic emission signals generated from journal bearing at different lubrication conditions based on wavelet analysis in combination with artificial neural network and genetic algorithm[J]. Tribology International, 2016, 95: 426-434.

[93] Changzheng C, Changcheng S, Yu Z, et al. Fault diagnosis for large-scale wind turbine rolling bearing using stress wave and wavelet analysis[C]//2005 International Conference on Electrical Machines and Systems. IEEE, 2005, 3: 2239-2244.

[94] Peter W T, Yang W, Tam H Y. Machine fault diagnosis through an effective exact wavelet analysis[J]. Journal of sound and vibration, 2004, 277(4-5): 1005-1024..

[95] Al-Raheem K F, Abdul-Karem W. Rolling bearing fault diagnostics using artificial neural networks based on Laplace wavelet analysis[J]. International Journal of Engineering, Science and Technology, 2010, 2(6).

[96] Samanta B, Al-Balushi K R, Al-Araimi S A. Artificial neural networks and genetic algorithm for bearing fault detection[J]. Soft Computing, 2006, 10(3): 264-271.

[97] Ali J B, Chebel-Morello B, Saidi L, et al. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network[J]. Mechanical Systems and Signal Processing, 2015, 56: 150-172.

[98] Gunerkar R S, Jalan A K, Belgamwar S U. Fault diagnosis of rolling element bearing based on artificial neural network[J]. Journal of Mechanical Science and Technology, 2019, 33(2): 505-511.

[99] Samanta B, Al-Balushi K R, Al-Araimi S A. Bearing fault detection using artificial neural networks and genetic algorithm[J]. EURASIP Journal on Advances in Signal Processing, 2004, 2004(3): 1-12.

[100] Kunli M, Yunxin W. Fault diagnosis of rolling element bearing based on vibration frequency analysis[C]//2011 Third International Conference on Measuring Technology and Mechatronics Automation. IEEE, 2011, 2: 198-201.

[101] Ahn J H, Kwak D H, Koh B H. Fault detection of a roller-bearing system through the EMD of a wavelet denoised signal[J]. Sensors, 2014, 14(8): 15022-15038.

[102] Soualhi A, Medjaher K, Zerhouni N. Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression[J]. IEEE Transactions on instrumentation and measurement, 2014, 64(1): 52-62.

[103] 王金东,刘著,赵海洋,张鹏,王智伟.基于MRSSD与MSDE的滑动轴承故障诊断方法[J].轴承,2020(09):50-56

[104] Nagata M, Fujita M, Yamada M, et al. Evaluation of tribological properties of bearing materials for marine diesel engines utilizing acoustic emission technique[J]. Tribology International, 2012, 46(1): 183-189.

[105] Sako T, Yoshie O. Diagnostic method of low speed rolling element bearing using AE envelope waveform[C]//TENCON 2010-2010 IEEE Region 10 Conference. IEEE, 2010: 724-729.

[106] 陆利威, 李新春, 张田. 基于声发射技术的滑动轴承故障诊断方法研究[J]. 机械工程与自动化, 2010, 163(06):123-127.

[107] 黄琪. 基于声发射检测的滑动轴承故障诊断方法研究[D]. 长沙理工大学, 2008.

[108] Leclère Q, Pezerat C, Laulagnet B, et al. Indirect measurement of main bearing loads in an operating diesel engine[J]. Journal of Sound & Vibration, 2005, 286(1–2):341-361..

[109] 余永华, 杨建国等. 柴油机滑动主轴承磨损的应变法监测[J]. 内燃机学报, 2002, 20(03):252-256.

[110] Spearot J A, Murphy C K. A comparison of the total capacitance and total resistance techniques for measuring the thickness of journal bearing oil films in an operating engine[J]. SAE transactions, 1988: 321-334.

[111] Suzuki S, Ozasa T, Noda T, et al. Analysis of con-rod big-end bearing lubrication on the basis of oil supply rate[R]. SAE Technical Paper, 1998.

[112] Katafuchi T, Kasai M. Effect of base stocks on the automobile engine bearing[J]. Tribology international, 2009, 42(4): 548-553.

[113] Ohta H, Nakajima Y, Kato S, et al. Vibration and acoustic emission measurements evaluating the separation of the balls and raceways with lubricating film in a linear bearing under grease lubrication[J]. Journal of Tribology, 2013, 135(4): 041104.

[114] Allmaier H, Priestner C, Six C, et al. Predicting friction reliably and accurately in journal bearings—A systematic validation of simulation results with experimental measurements[J]. Tribology International, 2011, 44(10): 1151-1160.

[115] Priestner C, Allmaier H, Priebsch H H, et al. Refined simulation of friction power loss in crank shaft slider bearings considering wear in the mixed lubrication regime[J]. Tribology International, 2012, 46(1): 200-207.

[116] Zhu J, Yang J G. Development trends of research on monitoring wear of sliding main bearing for diesel engine[C]//Advanced Materials Research. Trans Tech Publications Ltd, 2012, 472: 1702-1706.

[117] 孙波. 热电监测柴油机主轴承磨损状态的计算与试验[D].武汉理工大学,2014.

[118] 谢立康. 柴油机轴承磨损热—电法监测试验研究[D].武汉理工大学,2016.

[119] Sales, B. C. (2002). Smaller is cooler.(Perspectives: thermoelectric materials). Science, 295(5558), 1248-1250.

[120] 刘静.塞贝克系数的估算[J].科技创新与应用,2020(02):27-28.

[121] 刘明闯. 超导直流装置低漏热珀尔帖电流引线研究[D].华北电力大学(北京),2019

[122] 郑玮,汤磊.标准铂铑10-铂热电偶热电势约束公式探讨[J].计量学报,2020,41(02):175-178

[123] 温诗铸,黄平,田煜,等. 摩擦学原理. 第5版[M]. 北京:清华大学出版社,2018:19-22.

[124] Greenwood, J.A.; Tripp, J.H. The contact of two nominally flat rough surfaces. Arch. Proc. Inst. Mech. Eng. 1970, 185, 625–634.

[125] Mourelatos Z P. A crankshaft system model for structural dynamic analysis of internal combustion engines[J]. Computers & Structures, 2001, 79(20-21): 2009-2027.

[126] Paranjpe R S, Goenka P K. Analysis of crankshaft bearings using a mass conserving algorithm[J]. Tribology Transactions, 1990, 33(3): 333-344.

[127] 王宁,苏新兵,马斌麟,冯浩洋.网格类型对流场计算效率和收敛性的影响[J].空军工程大学学报(自然科学版),2018,19(01):9-14

[128] Williams, J.A.; Williams, John Engineering Tribology; Cambridge University Press: Cambridge, UK.

[129] 王金龙,魏文庆,崔大伟.48通道高速温度采集分析系统的设计与应用[J].热加工工艺,2013,42(17):48-51.

[130] 余永华. 船舶柴油机瞬时转速和热力参数监测诊断技术研究[D].武汉理工大学,2007

[131] 张立浩. 船用发动机缸压在线监测系统开发[D].武汉理工大学,2017

[132] Chen C, Zhang H, Technology S U. Detection of Bolt Tightness Abnormality Based on SMOTE and CART Decision Tree Theory[J]. Mechanical Engineer, 2019.

[133] Zhou Q, Zhou H, Li T. Cost-sensitive Feature Selection Using Random Forest: Selecting Low-Cost Subsets of Informative Features[J]. Knowledge-Based Systems, 2016:1-11.

[134] Minghua, Chen, Qunying, et al. XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System[J]. IEEE Access, 2019.

[135] Sun S, Huang R. An Adaptive K-nearest Neighbor Algorithm[C]. Seventh International Conference on Fuzzy Systems & Knowledge Discovery, IEEE, 2010.

中图分类号:

 TK421    

条码号:

 002000070364    

馆藏号:

 YD10001601    

馆藏位置:

 203    

备注:

 403-西院分馆博硕论文库;203-余家头分馆博硕论文库    

无标题文档

   建议浏览器: 谷歌 火狐 360请用极速模式,双核浏览器请用极速模式