数字孪生驱动的水电机组轴系智能维护

PDF(5687 KB)

振动与冲击 ›› 2024, Vol. 43 ›› Issue (15) : 189-199.

论文

数字孪生驱动的水电机组轴系智能维护

李佰霖1,2，陈思远1，唐淞1，付文龙1,2

作者信息 +

Intelligent maintenance of hydro-generator unit shafting driven by digital twin

LI Bailin1,2, CHEN Siyuan1, TANG Song1, FU Wenlong1,2

Author information +

文章历史 +

摘要

水电站设备运维数字化智能化是水电站高效运行的重要途径，相关研究处于初步阶段。学者们对其基本框架、内涵和技术路线展开了比较全面的阐述，但针对数字孪生模型的可实施性低、水电机组轴系故障库较少和轴系智能化维护水平较低等问题，相关内容还有待进一步研究。为了提高机组轴系智能化维护程度，本文以水力发电机组轴系为研究对象，构建其数字孪生驱动的智能维护模型及其结构，依托所提模型实现了以轴心轨迹故障库建立、轴系故障诊断、轴系可视化维护为主线的循环过程数字化。通过应用实践表明了数字孪生驱动的水电机组轴系智能维护的可行性，提高了水电智能化维护水平。

Abstract

Digital intelligence of hydropower plant equipment operation and maintenance is an important way for efficient operation of hydropower plants, and the related research is in the preliminary stage. Scholars have launched a relatively comprehensive exposition of its basic framework, connotation and technical route, but the relevant contents are still to be further studied for the problems of low implementability of digital twin model, less fault library of hydropower unit shafting and lower level of intelligent maintenance of shafting. In order to improve the level of intelligent maintenance of the shafting of the unit, this paper takes the shafting of the hydroelectric generating unit as the research object, constructs its digital twin-driven intelligent maintenance model and its structure, and relies on the proposed model to realize the digitalization of the cyclic process with the establishment of the fault library of the shaft trajectory, diagnosis of the shafting faults, and visualization of the shafting maintenance as the main line. The application practice shows the feasibility of digital twin-driven intelligent maintenance of hydropower unit shafting and improves the level of hydropower intelligent maintenance.

导出引用

李佰霖1,2，陈思远1，唐淞1，付文龙1,2. 数字孪生驱动的水电机组轴系智能维护[J]. 振动与冲击, 2024, 43(15): 189-199

LI Bailin1,2, CHEN Siyuan1, TANG Song1, FU Wenlong1,2. Intelligent maintenance of hydro-generator unit shafting driven by digital twin[J]. Journal of Vibration and Shock, 2024, 43(15): 189-199

参考文献

[1] LI H, BAI L, LUO X, et al. Multi-fractal feature recognition for shaft centerline orbit of hydropower units based on fuzzy clustering[J]. Shuili Fadian Xuebao/Journal of Hydroelectric Engineering, 2012, 31(4): 238-242+262. [2] SELAK L, BUTALA P, SLUGA A. Condition monitoring and fault diagnostics for hydropower plants[J]. Computers in Industry, 2014, 65(6): 924-936. [3] WANG C, ZHOU J, KOU P, et al. Identification of shaft orbit for hydraulic generator unit using chain code and probability neural network[J]. Applied Soft Computing Journal, 2012, 12(1): 423-429. [4] CHEN X, ZHOU J, XIAO H, et al. Fault diagnosis based on comprehensive geometric characteristic and probability neural network[J]. Applied Mathematics and Computation, 2014, 230: 542-554. [5] 郭明军, 李伟光, 杨期江等. 深度卷积神经网络在滑动轴承转子轴心轨迹识别中的应用[J]. 振动与冲击, 2021, 40(03): 233-239+283. GUO Mingjun, LI Weiguang, YANG Qijiang, et al. Application of deep convolution neural network in identification of journal bearing rotor center orbit[J]. Journal of Vibration and Shock, 2021, 40(03): 233-239+283. [6] 陈飞,王斌,周东东等.融合改进符号动态熵和随机配置网络的水电机组轴系故障诊断方法[J]. 水利学报, 2022,53(09): 1127-1139. CHEN Fei,WANG Bin, ZHOU Dongdong, et al. A fault diagnosis method for shaft system of hydropower units based on improved symbolic dynamic entropy and stochastic configuration network[J]. Journal of Hydraulic Engineering, 2022, 53(09): 1127-1139. [7] XIAO J, ZOU G, XIE J, et al. Identification of Shaft Orbit Based on the Grey Wolf Optimizer and Extreme Learning Machine[C]// XIAO J, ZOU G, XIE J, et al.2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, May 25, 2018 - May 27, 2018.Xi'an, China: Institute of Electrical and Electronics Engineers Inc, 2018: 1147-1150. [8] 李辉, 范智超, 李华, 等. 基于SVD和DBN的水电机组故障诊断[J]. 水力发电学报, 2020, 39(12): 104-112. LI Hui, FAN Zhichao, LI Hua, et al. Fault diagnosis of hydroelectric sets based on SVD and DBN [J]. Journal of Hydroelectric Engineering, 2020, 39(12): 104-112. [9] GE X, ZHANG J, ZHOU Y, et al. Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment[J]. Shock and Vibration, 2021, 2021. [10] AN X, LI C, ZHANG F. Application of adaptive local iterative filtering and approximate entropy to vibration signal denoising of hydropower unit[J]. Journal of Vibroengineering, 2016, 18(7): 4299-4311.. [11] GRIEVES M W. Product lifecycle management: the new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1): 1-8. [12] LEI Z, ZHOU H, HU W, et al. Toward a Web-Based Digital Twin Thermal Power Plant[J]. Ieee Transactions on Industrial Informatics, 2022, 18(3): 1716-1725. [13] TAO F, ZHANG M, LIU Y, et al. Digital twin driven prognostics and health management for complex equipment[J]. Cirp Annals-Manufacturing Technology, 2018, 67(1): 169-172. [14] 张社荣, 姜佩奇, 吴正桥. 水电工程设计施工一体化精益建造技术研究进展——数字孪生应用模式探索[J]. 水力发电学报, 2021, 40(01): 1-12. ZHANG Sherong, JIANG Peiqi, WU Zhengqiao. Advances in research of lean construction technology of integrated design and construction for hydropower projects: Exploration of digital twin application mode [J]. Journal of Hydroelectric Engineering, 2021, 40(1): 1-12. [15] 沈沉, 曹仟妮, 贾孟硕, 等. 电力系统数字孪生的概念、特点及应用展望[J]. 中国电机工程学报, 2022, 42(02): 487-499. SHEN Chen, CAO Qianni, JIA Mengshuo, et al. Concepts, Characteristics and Prospects of Application of Digital Twin in Power System[J]. Proceedings of the CSEE, 2022, 42(02): 1-12. [16] AZIMOV U, AVEZOVA N. Sustainable small-scale hydropower solutions in Central Asian countries for local and cross-border energy/water supply[J]. Renewable and Sustainable Energy Reviews, 2022, 167. [17] ZHAO Z, LI D, SHE J, et al. Construction and Application of Digital Twin Model of Hydropower Plant Based on Data-driven[C]// ZHAO Z, LI D, SHE J, et al.3rd International Workshop on Artificial Intelligence and Education, WAIE 2021, November 19, 2021 - November 21, 2021.Xi�an, China: Institute of Electrical and Electronics Engineers Inc, 2021: 60-64. [18] EBRAHIMI A. Challenges of developing a digital twin model of renewable energy generators[C]// EBRAHIMI A.28th IEEE International Symposium on Industrial Electronics, ISIE 2019, June 12, 2019 - June 14, 2019.Vancouver, BC, Canada: Institute of Electrical and Electronics Engineers Inc, 2019: 1059-1066. [19] MOUSSA C, AL-HADDAD K, KEDJAR B, et al. Insights into digital twin based on finite element simulation of a large hydro generator[C]// MOUSSA C, AL-HADDAD K, KEDJAR B, et al.44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018, October 20, 2018 - October 23, 2018.Washington, DC, United states: Institute of Electrical and Electronics Engineers Inc, 2018: 553-558. [20] LOPES T D, RAIZER A, JUNIOR W V. The use of digital twins in finite element for the study of induction motors faults[J]. Sensors, 2021, 21(23). [21] 任巍曦, 张文煜, 李明, 等. 基于数字孪生的风电机组轴承故障诊断方法研究[J]. 弹箭与制导学报, 2022, 42(03): 97-104. REN Weixi, ZHANG Wenyu, LI Ming, et al. Fault Diagnosis of Wind Turbine Bearing Based on Digital Twin[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2022 42(03):97-104. [22] LIU Z F, CHEN W, ZHANG C X, et al. Data Super-Network Fault Prediction Model and Maintenance Strategy for Mechanical Product Based on Digital Twin[J]. IEEE Access, 2019, 7: 177284-177296. [23] ZHAO H, HU W, LIU Z, et al. A capsnet-based fault diagnosis method for a digital twin of a wind turbine gearbox[C]// ZHAO H, HU W, LIU Z, et al.ASME 2021 Power Conference, POWER 2021, July 20, 2021 - July 22, 2021.Virtual, Online: American Society of Mechanical Engineers (ASME), 2021: Power Division. [24] JAHANGIRI V, VALIKHANI M, EBRAHIMIAN H, et al. Digital Twinning of Modeling for Offshore Wind Turbine Drivetrain Monitoring: A Numerical Study[C]// JAHANGIRI V, VALIKHANI M, EBRAHIMIAN H, et al.40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, February 7, 2022 - February 10, 2022.Orlando, FL, United states: Springer, 2023: 135-137. [25] JONNE G B, IBRAHIM D, RAI L, et al. Development of Fault Diagnostics, and Prognosis System based on Digital Twin and Blockchain[C]// JONNE G B, IBRAHIM D, RAI L, et al.1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021, November 29, 2021 - November 30, 2021.Virtual, Online, United states: Institute of Physics, 2022: 13603-13611. [26] REGIS A, LINARES J-M, ARROYAVE-TOBON S, et al. Numerical model to predict wear of dynamically loaded plain bearings[J]. Wear, 2022, 508-509. [27] WANG Z, JIA W, WANG K, et al. Digital twins supported equipment maintenance model in intelligent water conservancy[J]. Computers and Electrical Engineering, 2022, 101. [28] VORONIN S, DAVLATOV A, KOSIMOV B. Development directions of power supply for rural areas of Tajikistan[C]// VORONIN S, DAVLATOV A, KOSIMOV B.2019 International Ural Conference on Electrical Power Engineering, UralCon 2019, October 1, 2019 - October 3, 2019.Chelyabinsk, Russia: Institute of Electrical and Electronics Engineers Inc, 2019: 157-161. [29] 陈雨甜, 王秀丽, 钱涛等. 基于广义斯塔克尔伯格博弈的微电网能源管理[J].电力自动化设备, 2022, 42(01): 171-177. CHEN Yutian, WANG Xiuli, QIAN Tao, et al. Energy management of microgrid based on generalized Stackelberg game[J]. Electric Power Automation Equipment, 2022, 42(01): 171-177. [30] 徐波,张春辉,李友平等.立式水电机组轴线智能调整系统研发及应用[J].水力发电学报,2023,42(05):67-76. XU Bo, zhang Chunhui, li youping, et al. Development and application of intelligent axis adjustment system for vertical hydro-generator unit[J]. Journal of Hydroelectric Engineering, 2023,42(05):67-76. [31] 邓力源,杨萍,刘卫东等.基于证据理论层次分析法的云贝叶斯网络在预警雷达毁伤效果评估中的应用[J]. 兵工学报, 2022, 43(04): 814-825. DENG Liyuan, YANG Ping, LIU Weidong, et al. Application in Damage Effect Evaluation of Early Warning Radar of Cloudy Bayesian Network Based on Dempster-Shafer/Analytic Hierarchy Process Method[J]. Acta Armamentarii, 2022, 43(04): 814-825. [32] 张腾,马荣国.高铁引线绩效多层次模糊综合评价方法[J].交通运输工程学报, 2011, 11(02): 97-101. ZHANG Teng, MA Rongguo. Multi-level fuzzy comprehensive evaluation method of lead performance for high-speed railway[J]. Journal of Traffic and Transportation Engineering, 2011, 11(02): 97-101. [33] 张雷克,范宇宏,张金剑等.水轮发电机组横/轴有限元振动分析[J].振动与冲击,2022,41(14):64-69+98. ZHANG Leike, FAN Yuhong, ZHANG Jinjian, et al. Finite element analysis on the transversal / axial vibration of a hydraulic generating set[J]. Journal of Vibration and Shock, 2022, 41(14): 64-69+98. [34] 徐永.大型水轮发电机组轴系动力学建模与仿真分析[D]. 武汉: 华中科技大学, 2012. XU Yong. Dynamic Modeling and Simulation Analysis of the Shaft System for Large Hydro-turbine Generator Units[D]. Wuhan: Huazhong University of Science and Technology, 2012.