智能船舶代表了船舶未来的发展方向，柴油机作为船舶主要驱动设备，其故障诊断对智能船舶的安全性和可靠性具有重要意义。人工智能有效地解决了传统故障诊断方法存在的问题，基于人工智能方法的柴油机故障诊断技术成为近些年的研究热点。本文总结人工智能在柴油机故障诊断中的研究进展，对迁移学习应用到柴油机故障诊断中的前景进行展望，分析了柴油机故障诊断的研究趋势和面临的挑战。
Intelligent ships represent the future development direction of ships. As the main driving equipment of ships, diesel engines are of great significance to the safety and reliability of intelligent ships. Artificial intelligence technology is superior to traditional methods in marine diesel engine fault diagnosis. The application of this technology in diesel engines has become a research hotspot in recent years. Accordingly, this paper summarizes and prospects the development and application of diesel engine artificial intelligent fault diagnosis, and the research trends and challenges of diesel engine fault diagnosis are analyzed.
2023,45(24): 122-127 收稿日期：2022-08-19
DOI：10.3404/j.issn.1672-7649.2023.24.022
分类号：TK428
作者简介：殷文慧(1997-),女,硕士,助理工程师,研究方向为无人系统健康管理及人工智能在船舶领域的应用
参考文献：
[1] 刘志林, 苑守正, 郑林熇, 等. 船舶自动靠泊技术的发展现状和趋势[J]. 中国造船, 2021, 62(4): 293-304
[2] 张笛, 赵银祥, 崔一帆, 等. 智能船舶的研究现状可视化分析与发展趋势[J]. 交通信息与安全, 2021, 39(1): 7-16
[3] 杨建国, 王晓武. 船舶柴油机监测与故障诊断技术现状及发展趋势[J]. 中国航海, 1999(2): 43-50
[4] 陈雪峰. 智能运维与健康管理[M]. 北京: 机械工业出版社, 2018.
[5] 马海洲, 丁爱萍. 人工智能技术在船舶动力装置故障诊断中的应用[J]. 舰船科学技术, 2021, 43(12): 109-111
MA Hai-zhou, DING Ai-ping. Application of artificial intelligence technology in fault diagnosis of ship power unit[J]. Ship Science and Technology, 2021, 43(12): 109-111
[6] 桂士宏, 邹念洋, 李楠. 国外军用领域人工智能发展规划及舰船智能化技术运用[J]. 舰船科学技术, 2021, 43(18): 124-126
GUI Shi-hong, ZOU Nian-yang, LI Nan. Development planning of artificial intelligence in foreign military field and application of ship intelligence technology in abroad[J]. Ship Science and Technology, 2021, 43(18): 124-126
[7] 李杰. 工业人工智能[M]. 上海: 上海交通大学出版社, 2019.
[8] 刘博, 王彦辉, 王晶. 柴油机故障诊断技术的现状及展望[J]. 内燃机与配件, 2022(2): 131-133
[9] 刘世伟. 船舶柴油机故障诊断技术发展现状与趋势分析[J]. 内燃机与配件, 2018(6): 146-147
[10] 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
[11] 孙宇航, 崔策, 许少凡, 等. 基于大数据的设备故障智能诊断技术研究综述[J]. 设备管理与维修, 2022(13): 150-151
[12] LEI Y, YANG B, JIANG X, et al. Applications of machine learning to machine fault diagnosis: A review and roadmap[J]. Mechanical Systems and Signal Processing, 2020, 138: 106587
[13] 雷亚国. 旋转机械智能故障诊断与剩余寿命预测(英文版)[M]. 西安: 西安交通大学出版社, 2017.
[14] WU J D, LIU C H. An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network[J]. Expert Systems with Applications, 2009, 36(3-part-P1): 4278-4286
[15] WU J D, CHIANG P H, CHANG Y W, et al. An expert system for fault diagnosis in internal combustion engines using probability neural network[J]. Expert Systems with Applications, 2008, 34(4): 2704-2713
[16] GELGELE H L, WANG K. An expert system for engine fault diagnosis: development and application[J]. Journal of Intelligent Manufacturing, 1998, 9(6): 539-545
[17] 臧军, 马善伟, 刘赟. 船舶柴油机故障诊断专家系统研制[J]. 柴油机, 2011, 33(3): 25-28+45
[18] 何正嘉, 陈进, 王太勇, 等. 机械故障诊断理论及应用[M]. 北京: 高等教育出版社, 2010.
[19] SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85-117
[20] SHARKEY A J C, CHANDROTH G O, SHARKEY N E. A Multi-Net System for the Fault Diagnosis of a Diesel Engine[J]. Neural Computing & Applications, 2000, 9(2): 152-160
[21] 孟宪尧, 韩新洁, 孟松. 优化的BP网络在船舶故障诊断中的应用[J]. 中国航海, 2007(2): 85-88
[22] WU J D, HUANG C K, CHANG Y W, et al. Fault diagnosis for internal combustion engines using intake manifold pressure and artificial neural network[J]. Expert Systems with Applications An International Journal, 2010, 37(2): 949-958
[23] WU J D, HUANG C K. An engine fault diagnosis system using intake manifold pressure signal and Wigner-Ville distribution technique[J]. Pergamon Press, Inc., 2011, 38(1): 536-544
[24] 徐晓健. 船舶动力系统故障诊断方法与趋势预测技术研究[D]. 武汉: 武汉理工大学, 2014.
[25] 刘健康, 高文志, 张攀, 等. 基于改进段角加速度和神经网络的柴油机失火诊断研究[J]. 内燃机工程, 2019, 40(1): 79-85
[26] 张攀, 高文志, 高博, 等. 基于人工神经网络的柴油机失火故障诊断[J]. 振动. 测试与诊断, 2020, 40(4): 702-710+823
[27] 曹欢, 胡磊, 谢文琪, 等. 基于多源信息融合的柴油机故障诊断方法[J]. 造船技术, 2020(1): 73-80+92.
CAO Huan, HU Lei, XIE Wen-qi, et al. Diesel engine fault diagnosis method based on multi-source information fusion[J]. Marine Technology, 2020(1): 73-80+92.
[28] NIU X, YANG C, WANG H, et al. Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine[J]. Applied Thermal Engineering, 2016: S952694662.
[29] ZHAN Y L, WEI W, HUO C F, et al. Research on delamination fault diagnosis of marine diesel engine based on Support Vector Machine[C]//2009 IEEE International Symposium on Industrial Electronics. IEEE, 2009: 1224-1227.
[30] 陈杰. 基于支持向量机的柴油机故障诊断技术研究[D]. 武汉: 武汉理工大学, 2012.
[31] 柴艳有. 基于核学习理论的船舶柴油机故障诊断研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
[32] 刘学坤. 基于支持向量机和油液检测的船舶柴油机故障诊断研究[D]. 大连: 大连海事大学, 2013.
[33] 韩孝坤, 李霏. 基于支持向量机和油液检测的船舶柴油机故障诊断研究[J]. 科技风, 2017(26): 115
[34] CAI C, ZHANG C, GANG L. A novel fault diagnosis approach combining SVM with association rule mining for ship diesel engine[C]//2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design(CSCWD). IEEE, 2016: 400-405.
[35] 汪明, 胡海刚, 张刚, 等. 基于小波包Shannon熵与GA-SVM的船舶轴系故障诊断方法[J]. 宁波大学学报(理工版), 2015, 28(4): 124-128
[36] 蔡一杰, 陈俊杰, 王君, 等. 基于遗传算法优化支持向量机的船用柴油机气门漏气故障智能诊断方法[J]. 内燃机工程, 2022, 43(2): 71-76
[37] 张俊红, 刘昱, 毕凤荣, 等. 基于LMD和SVM的柴油机气门故障诊断[J]. 内燃机学报, 2012, 30(5): 809-813
[38] 张志政. 基于优化KPCA-SVM的船舶燃油系统故障监测和诊断研究[D]. 大连: 大连海事大学, 2020.
[39] 乔新勇, 顾程, 韩立军. 基于VMD多尺度散布熵的柴油机故障诊断方法[J]. 汽车工程, 2020, 42(8): 1139-1144
[40] 张志政, 王冬捷, 张勇亮. 基于PSO改进KPCA-SVM的故障监测和诊断方法研究[J]. 现代制造工程, 2020(9): 101-107
[41] HOU L, ZHANG J, DU B. A Fault diagnosis model of marine diesel engine fuel oil supply system using PCA and optimized SVM[J]. Journal of Physics:Conference Series, 2020, 1576(1): 12045-12048
[42] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554
[43] HINTON, G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks.[J]. Science, 2006, 313(5786): 504-507
[44] BENGIO Y. Learning deep architectures for AI[M]. Now Publishers Inc. , 2009.
[45] 雷亚国, 贾峰, 周昕, 等. 基于深度学习理论的机械装备大数据健康监测方法[J]. 机械工程学报, 2015, 51(21): 49-56
[46] 邱锡鹏. 神经网络与深度学习[M]. 北京: 机械工业出版社, 2020.
[47] 仲国强, 贾宝柱, 肖峰, 等. 基于深度信念网络的船舶柴油机智能故障诊断[J]. 中国舰船研究, 2020, 15(3): 136-142
[48] 黄金娥, 刘鹏鹏. 基于改进深度学习算法的船舶柴油机故障诊断技术[J]. 舰船科学技术, 2021, 43(7): 131-134
[49] 黄鑫, 陈仁祥, 黄钰. 卷积神经网络在机械设备故障诊断领域应用与挑战[J]. 制造技术与机床, 2019(1): 96-100
[50] 张康, 陶建峰, 覃程锦, 等. 随机丢弃和批标准化的深度卷积神经网络柴油机失火故障诊断[J]. 西安交通大学学报, 2019, 53(8): 159-166
[51] 廖玉诚, 赵建华, 安士杰, 等. 基于2DCNN的柴油机供油提前角异常故障诊断[J]. 兵器装备工程学报, 2021, 42(5): 250-255
[52] 靳莹, 乔新勇, 顾程, 等. 基于Res-CNN和燃油压力波的柴油机喷油器故障诊断方法[J]. 汽车工程, 2021, 43(6): 943-951
[53] 朱继安, 匡青云, 刘义. 改进ResNet网络模型进行柴油机气门间隙故障诊断的方法[J]. 移动电源与车辆, 2022, 53(1): 31-39
[54] PAN S J, QIANG Y. A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
[55] 王晓东. 基于迁移学习的动车组轴承故障诊断方法研究[D]. 北京: 北京交通大学, 2021.
[56] SHEN F, CHEN C, YAN R, et al. Bearing fault diagnosis based on SVD feature extraction and transfer learning classification[C]//2015 prognostics and system health management conference (PHM). IEEE, 2015: 1-6.
[57] HAN T, LIU C, YANG W, et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults[J]. Knowledge-Based Systems, 2019, 165(2): 474-487
[58] GUO L, LEI Y, XING S, et al. Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data[J]. IEEE Transactions on Industrial Electronics, 2019, 66(9): 7316-7325
[59] YANG B, LEI Y, JIA F, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings[J]. Mechanical Systems and Signal Processing, 2019, 122: 692-706
[60] BAI M, YANG X, LIU J, et al. Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers[J]. Applied Energy, 2021, 302.
[61] SOBIE C, FREITAS C, NICOLAI M. Simulation-driven machine learning: Bearing fault classification[J]. Mechanical Systems and Signal Processing, 2018, 99: 403-419
[62] GAO Y, LIU X, XIANG J. FEM Simulation- Based Generative Adversarial Networks to Detect Bearing Faults[J]. IEEE Transactions on Industrial Informatics, 2020(99): 1
[63] FARHAT M H, CHIEMENTIN X, CHAARI F, et al. Digital twin-driven machine learning: ball bearings fault severity classification[J]. Measurement Science and Technology, 2021, 32(4): 44006-44014
