故障诊断和预测技术及其在水下无人系统的应用

程文鑫; 肖安康

doi:10.11993/j.issn.2096-3920.2024-0064

故障诊断和预测技术及其在水下无人系统的应用

doi: 10.11993/j.issn.2096-3920.2024-0064

海军装备部装备项目管理中心, 北京, 100071

详细信息

作者简介:
程文鑫(1977-), 男, 博士, 高级工程师, 武器系统与运用工程

中图分类号: TJ630.1; TB71.2
计量
- 文章访问数: 12
- HTML全文浏览量: 4
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2016-11-19
- 修回日期: 2016-12-18
- 录用日期: 2024-05-22
- 网络出版日期: 2024-06-11

Fault Diagnosis and Prediction Techniques in the Field of Undersea Unmanned System

Program Management Center, Naval Armament Department, Beijing100071, China

摘要

摘要: 水下无人系统作为一个有机的整体, 其集成化程度越高、结构越复杂, 出现故障的可能性就越大, 由此带来的风险及损失也会越大。故障诊断和预测技术在提升水下无人系统可靠性与安全性方面具有重要意义。本文对国内外故障诊断与故障预测方法研究现状进行调研, 概述了故障诊断和预测技术在水下无人系统领域的应用。
- 水下无人系统 /
- 故障诊断 /
- 故障预测
Abstract: As an integrated organic entity, underwater unmanned systems become more prone to faults as their level of integration increases and their structures become more complex. Consequently, the associated risks and potential losses escalate. The application of fault diagnosis and prediction techniques plays a crucial role in enhancing the reliability and safety of underwater unmanned systems. This paper surveys the current research status of fault diagnosis and prediction methods both domestically and internationally, providing an overview of their application within the field of underwater unmanned systems.
- undersea unmanned system /
- fault diagnosis /
- fault prediction

HTML全文

图 1 故障诊断方法分类

Figure 1. Classification of fault diagnosis methods

下载: 全尺寸图片幻灯片

参考文献(86)

[1]	李玥迪. 基于水下无人系统的电机故障诊断技术研究[D]. 黑龙江: 哈尔滨工业大学, 2023.
[2]	焦林健. 大尺度水下无人艇定深及安全线路跟踪研究[D]. 湖北: 华中科技大学, 2022.
[3]	黄琰, 李岩, 俞建成, 等. AUV智能化现状与发展趋势[J]. 机器人, 2020, 42(2): 215-231. Huang Yan, Li Yan, Yu Jiancheng, et al. State-of-the-art and developing trends of AUV intelligence[J]. Robot, 2020, 42(2): 215-231.
[4]	许世坤. 基于滑模理论的UUV轨迹跟踪与主动容错控制研究[D]. 吉林: 吉林大学, 2021.
[5]	张宝贵, 周俊. UUV应急处理策略构建推理研究[J]. 数字海洋与水下攻防, 2021, 4(2): 139-142.
[6]	郭恩全, 陈晓明, 张鑫. 水中兵器综合测试与故障诊断技术[C]//2011年振动与噪声测试峰会论文集. 北京: 中国电子学会, 2011: 194-199.
[7]	郭小溪. 水下无人航行器故障预测与健康管理框架研究[J]. 科技风, 2021(21): 189-190.
[8]	曹飞, 叶枫桦, 于宪龙. 无人水下航行器故障预测与健康管理框架研究[J]. 数字海洋与水下攻防, 2020, 3(4): 350-354.
[9]	赵博, 王天宇, 张润峰. 基于模糊相关向量机的UUV中缓变故障检测方法[J]. 计算机与数字工程, 2023, 51(4): 838-844.
[10]	Bian X Q, Chen T, Yan Z P, et al. Fault diagnosis based on grey dynamic prediction for AUV sensor[C]//2009 IEEE International Conference on Industrial Technology (ICIT). Churchill, VIC, Australia: IEEE, 2009.
[11]	韩云东, 周明, 童艳, 等. 基于案例推理的鱼雷制导系统故障诊断方法[J]. 鱼雷技术, 2012, 20(2): 129-133.
[12]	Zhou Z. Research on key technology of prognostic and health management for autonomous underwater vehicle[C]//2017 International Conference on Robotics and Machine Vision. Kitakyushu, Japan: SPIE, 2017.
[13]	Lu W, Liang B, Cheng Y, et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics. 2016. 64(3): 2296-2305
[14]	Wen L, Gao L, Li X. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017. 49(1): 136-144
[15]	Pan S J, Yang Q. A Survey on Transfer Learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359 doi: 10.1109/TKDE.2009.191
[16]	Ramesh T S, Shum S K, Davis J F. A structured framework for efficient problem solving in diagnostic expert systems[J]. Computers & Chemical Engineering. 1988, 12(9-10): 891-902
[17]	Wilcox N A, Himmelblau D. M. The possible cause and effect graphs(PCEG) model for fault diagnosis—I. Methodology[J]. Computers & chemical engineering, 1994, 18(2): 103-116
[18]	Palshikar G K. Temporal fault trees[J]. Information and Software Technology, 2002, 44(3): 137-150 doi: 10.1016/S0950-5849(01)00223-3
[19]	彭华亮, 沈暑龙, 李军, 等. 基于故障树的故障诊断专家系统设计[J]. 控制工程, 2019, 26(3): 584-588. Peng Hualiang, Shen Shulong, Li Jun, et al. Design of Diagnostic Expert System for Launch Vehicles Based on FTA[J]. Control Engineering of China, 2019, 26(3): 584-588.
[20]	周东华, 胡艳艳. 动态系统的故障诊断技术[J]. 自动化学报, 2009, 35(6): 748-758. doi: 10.3724/SP.J.1004.2009.00748 Zhou Donghua, Hu Yanyan. Review on fault diagnosis techniques for closed-loop systems[J]. Acta Automatica Sinica, 2009, 35(6): 748-758. doi: 10.3724/SP.J.1004.2009.00748
[21]	Watanabe K, Himmelblau D M. Instrument fault detection in systems with uncertainties[J]. International Journal of Systems Science, 1982, 13(2): 137-158. doi: 10.1080/00207728208926337
[22]	张栋, 李春涛, 杨艺. 基于多模型参数估计的舵机故障诊断算法[J]. 电光与控制, 2014, 21(12): 85-89. Zhang Dong, Li Chuntao, Yang Yi. Actuator failure diagnosis based on multi-model parameter estimation[J]. Electronics Optics & Control, 2014, 21(12): 85-89.
[23]	Chow E, Willsky A. Analytical redundancy and the design of robust failure detection systems[J]. IEEE Transactions on automatic control, 1984, 29(7): 603-614. doi: 10.1109/TAC.1984.1103593
[24]	郑小霞, 钱锋. 动态系统故障诊断技术的研究与发展[J]. 化工自动化及仪表, 2005(4): 1-7. Zheng Xiaoxia, Qian Feng. Research and Development of Fault Diagnosis Methods for Dynamic System[J]. Control and Instruments in Chemical Industry, 2005(4): 1-7.
[25]	强子健, 鲁峰, 常晓东, 等. 基于二阶鲁棒滑模观测器的民用涡扇发动机气路故障诊断[J]. 推进技术, 2020, 41(6): 1411-1419. Qiang Zijian, Lu Feng, Chang Xiaodong, et al. Gas path fault diagnosis based on second-order robust sliding mode observer for civil turbofan engine[J]. Journal of Propulsion Technology, 2020, 41(6): 1411-1419.
[26]	丁琦. 基于解析模型的非线性系统故障诊断方法研究[D]: 厦门: 厦门大学, 2018.
[27]	李晗, 萧德云. 基于数据驱动的故障诊断方法综述[J]. 控制与决策, 2011, 26(1): 1-9,16. Li Han, Xiao Deyun. Survey on data driven fault diagnosis methods[J]. Control and Decision, 2011, 26(1): 1-9,16.
[28]	Montgomery D C. Introduction to statistical quality control[M]. USA: John Wiley & Sons, 2007.
[29]	Nandi S, Toliyat H A, Li X. Condition monitoring and fault diagnosis of electrical motors—A review[J]. IEEE Transactions on Energy Conversion, 2005, 20(4): 719-729. doi: 10.1109/TEC.2005.847955
[30]	alexander g, jean m. decomposition of hardy functions into square integrable wavelets of constant shape[J]. SIAM Journal on Mathematical Analysis, 1984, 15(4): 723-736. doi: 10.1137/0515056
[31]	Rajakarunakaran S, Venkumar P, Devaraj D, et al. Artificial neural network approach for fault detection in rotary system[J]. Applied Soft Computing, 2008, 8(1): 740-748. doi: 10.1016/j.asoc.2007.06.002
[32]	Siddique A, Yadava G S, Singh B. Applications of artificial intelligence techniques for induction machine stator fault diagnostics[C]//4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. Atlanta, GA, USA: IEEE, 2003: 29-34.
[33]	胡杰, 严勇杰, 单尧. 基于CUSUM控制图的电离层慢变故障检测方法[J]. 指挥信息系统与技术, 2019, 10(1): 49-54. Hu Jie, Yan Yongjie, Shan Yao. Ionospheric slow-growing error detection method based on CUSUM control chart[J]. Command Information System and Technology, 2019, 10(1): 49-54.
[34]	Wold S, Esbensen K, Geladi P. Principal component analysis[J]. Chemometrics and Intelligent Laboratory Systems, 1987, 2(1-3): 37-52. doi: 10.1016/0169-7439(87)80084-9
[35]	Mika S, Ratsch G, Weston J, et al. Fisher discriminant analysis with kernels[C]//Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop. Madison, WI, USA: IEEE, 1999.
[36]	Boashash B. Time frequency signal analysis: Past, present and future trends[J]. Control and Dynamic Systems, 1996, 78: 1-69.
[37]	Daubechies I. The wavelet transform, time-frequency localization and signal analysis[J]. IEEE transactions on information theory. 1990. 36(5): 961-1005.
[38]	Lin J, Zuo M J. Gearbox fault diagnosis using adaptive wavelet filter[J]. Mechanical systems and signal processing, 2003, 17(6): 1259-1269. doi: 10.1006/mssp.2002.1507
[39]	徐超. 基于Hilbert-Huang变换的磁悬浮转子跌落振动信号检测[D]. 吉林: 吉林大学, 2018.
[40]	Huang N E, Shen S S P. Hilbert-Huang transform and its applications[M]. Singapore: World Scientific, 2014.
[41]	Jegadeeshwaran R, Sugumaran V. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines[J]. Mechanical Systems and Signal Processing, 2015, 52: 436-446.
[42]	Zhang X L, Chen W, Wang B J, et al. Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization[J]. Neurocomputing, 2015, 167: 260-279. doi: 10.1016/j.neucom.2015.04.069
[43]	Jung U, Koh B. Wavelet energy-based visualization and classification of high-dimensional signal for bearing fault detection[J]. Knowledge and Information Systems, 2015, 44(1): 197-215. doi: 10.1007/s10115-014-0761-z
[44]	Lu C, Wang Z Y, Qin W L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130: 377-388. doi: 10.1016/j.sigpro.2016.07.028
[45]	Lei Y G, Li N P, Guo L, et al. Machinery health prognostics: A systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 799-834. doi: 10.1016/j.ymssp.2017.11.016
[46]	Lee J, Wu F, Zhao W, et al. Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications[J]. Mechanical Systems and Signal Processing, 2014, 42(1-2): 314-334. doi: 10.1016/j.ymssp.2013.06.004
[47]	Kan M S, Tan A, Mathew J. A review on prognostic techniques for non-stationary and non-linear rotating systems[J]. Mechanical Systems and Signal Processing, 2015, 62: 1-20.
[48]	Paris P, Erdogan F. A critical analysis of crack propagation laws[J]. Journal of Basic Engineering, 1963, 85(4): 528-533. doi: 10.1115/1.3656900
[49]	Qian Y, Yan R, Gao R. A multi-time scale approach to remaining useful life prediction in rolling bearing[J]. Mechanical Systems and Signal Processing, 2017, 83: 549-567. doi: 10.1016/j.ymssp.2016.06.031
[50]	牛乾. 机械旋转部件的性能退化及其寿命预测方法研究[D]. 杭州: 浙江大学, 2018.
[51]	Ashwin T R, McGordon A, Jennings P A. Electrochemical modelling of Li-ion battery pack with constant voltage cycling[J]. Journal of Power Sources, 2017, 341: 327-339. doi: 10.1016/j.jpowsour.2016.11.092
[52]	孙建. 滚动轴承振动故障特征提取与寿命预测研究[D]. 大连: 大连理工大学, 2015
[53]	陈坤龙. 基于数据驱动的锂电池剩余寿命预测方法研究[D]. 北京: 北京交通大学, 2018.
[54]	Severson K A, Attia P M, Jin N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4(5): 383-391. doi: 10.1038/s41560-019-0356-8
[55]	Schwabacher M A. A survey of data-driven prognostics[C]// InfoTech @ Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration. Arlington, Virginia, USA: AIAA, 2005: 887-891.
[56]	Gebraeel N Z, Lawley M A. A neural network degradation model for computing and updating residual life distributions[J]. IEEE Transactions on Automation Science and Engineering, 2008, 5(1): 154-163. doi: 10.1109/TASE.2007.910302
[57]	姜媛媛, 刘柱, 罗慧, 等. 锂电池剩余寿命的ELM间接预测方法[J]. 电子测量与仪器学报, 2016, 30(2): 179-185. Jiang Yuanyuan, Liu Zhu, Luo Hui, et al. ELM indirect prediction method for the remaining life of lithium-ion battery[J]. Journal of Electronic Measurement and Instrumentation, 2016, 30(2): 179-185.
[58]	刘子英, 朱琛磊. 基于Elman神经网络模型的IGBT寿命预测[J]. 半导体技术, 2019, 44(5): 395-400. Liu Ziying, Zhu Chenlei. IGBT life prediction based on elman neural network model[J]. Semiconductor Technology, 2019, 44(5): 395-400.
[59]	奚立峰, 黄润青, 李兴林, 等. 基于神经网络的球轴承剩余寿命预测[J]. 机械工程学报, 2007, 43(10): 137-143. doi: 10.3901/JME.2007.10.137 Xi Lifeng, Huang Runqing, Li Xinglin, et al. Residual life predictions for ball bearing based on neural networks[J]. Journal of Mechanical Engineering, 2007, 43(10): 137-143. doi: 10.3901/JME.2007.10.137
[60]	Khumprom P, Yodo N. A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm[J]. Energies, 2019, 12(4): 1-21.
[61]	Yann L C, Yoshua B, Geoffrey H. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
[62]	张继冬, 邹益胜, 邓佳林, 等. 基于全卷积层神经网络的轴承剩余寿命预测[J]. 中国机械工程, 2019, 30(18): 2231-2235. Zhang Jidong, Zou Yisheng, Deng Jialin, et al. Bearing Remaining Life Prediction Based on Full Convolutional Layer Neural Networks[J]. China Mechanical Engineering, 2019, 30(18): 2231-2235.
[63]	Malhi A, Yan R, Gao R X. Prognosis of defect propagation based on recurrent neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2011, 60(3): 703-711. doi: 10.1109/TIM.2010.2078296
[64]	Li X, Zhang L, Wang Z, et al. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks[J]. Journal of Energy Storage, 2019, 21: 510-518. doi: 10.1016/j.est.2018.12.011
[65]	Chen C, Zhang B, Vachtsevanos G, et al. Machine condition prediction based on adaptive neuro–fuzzy and high-order particle filtering[J]. IEEE Transactions on Industrial Electronics, 2010, 58(9): 4353-4364.
[66]	Acuña D E, Orchard M E. Particle-filtering-based failure prognosis via sigma-points: Application to lithium-ion battery state-of-charge monitoring[J]. Mechanical Systems and Signal Processing, 2017, 85: 827-848. doi: 10.1016/j.ymssp.2016.08.029
[67]	Hu C, Youn B D, Wang P, et al. Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life[J]. Reliability Engineering & System Safety, 2012, 103: 120-135.
[68]	Mosallam A, Medjaher K, Zerhouni N. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction[J]. Journal of Intelligent Manufacturing, 2016, 27(5): 1037-1048. doi: 10.1007/s10845-014-0933-4
[69]	Lu W, Liang B, Cheng Y, et al. Deep model based domain adaptation for fault diagnosis[J]. IEEE Transactions on Industrial Electronics, 2016, 64(3): 2296-2305.
[70]	Wen L, Gao L, Li X. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 49(1): 136-144.
[71]	陈超, 沈飞, 严如强. 改进LSSVM迁移学习方法的轴承故障诊断[J]. 仪器仪表学报, 2017, 38(1): 33-40. Chen Chao, Shen Fei, Yan Ruqiang. Enhanced least squares support vector machine-based transfer learning strategy for bearing fault diagnosis[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 33-40.
[72]	胡明武. 基于迁移学习的变工况下滚动轴承故障诊断方法研究[D]: 哈尔滨: 哈尔滨理工大学, 2019.
[73]	Hu Q, Zhang R, Zhou Y. Transfer learning for short-term wind speed prediction with deep neural networks[J]. Renewable Energy, 2016, 85: 83-95. doi: 10.1016/j.renene.2015.06.034
[74]	Ribeiro M, Grolinger K, ElYamany H F, et al. Transfer learning with seasonal and trend adjustment for cross-building energy forecasting[J]. Energy & Buildings, 2018, 165: 352-363.
[75]	Ulyanov D, Vedaldi A, Lempitsky V. Deep image prior[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018.
[76]	Xie Y, Wang Z, Ji S. Noise2Same: optimizing a self-supervised bound for image denoising[J]. Advances in Neural Information Processing Systems, 2020, 2020,33: 20320-20330.
[77]	Laine S, Karras T, Lehtinen J, et al. High-quality self-supervised deep image denoising[J]. Advances in Neural Information Processing Systems, 2019, 32: 6970-6980.
[78]	Krull A, Buchholz T O, Jug F. Noise2Void-learning denoising from single noisy images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE, 2019: 2129-2137.
[79]	Devlin J, Chang M, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019: 4171-4186.
[80]	Hassani K, Khasahmadi A H. Contrastive multi-view representation learning on graphs[C]//Proceedings of the 37th International Conference on Machine Learning. Vienna, Austria: ICML, 2020.
[81]	Luo L, Wang X, Guo H, et al. Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907-2017)[J]. Remote Sensing of Environment, 2019, 232: 111280. doi: 10.1016/j.rse.2019.111280
[82]	Nagla K, Uddin M, Singh D. Multisensor data fusion and integration for mobile robots: A review[J]. IAES International Journal of Robotics and Automation, 2014, 3(2): 131.
[83]	Dinh N V, Kim G W. Multi-sensor fusion towards VINS: A concise tutorial, survey, framework and challenges[J]. 2020 IEEE International Conference on Big Data and Smart Computing. Busan, Korea (South): IEEE, 2020: 459-462.
[84]	Soilán M, Sánchez-Rodríguez A, del Río-Barral P, et al. Review of laser scanning technologies and their applications for road and railway infrastructure monitoring[J]. Infrastructures, 2019, 4(4): 58. doi: 10.3390/infrastructures4040058
[85]	Rashdi R, Martínez-Sánchez J, Arias P, et al. Scanning technologies to building information modelling: A review[J]. Infrastructures, 2022, 7(4): 49. doi: 10.3390/infrastructures7040049
[86]	Trubia S, Severino A, Curto S, et al. Smart roads: An overview of what future mobility will look like[J]. Infrastructures, 2020, 5(12): 107. doi: 10.3390/infrastructures5120107