Health Status Assessment on the Operation Function of Nuclear Power Plants Based on Multi-level Data Fusion

doi:10.3901/JME.2024.04.200

Abstract

Abstract: With the new-generation information technologies and artificial intelligence that are deeply integrated with the industry, the maintenance of industrial systems is moving from the manual regular maintenance to state-based intelligent maintenance(IM). Health status assessment(HSA) is a key link in IM. Systemic states are recognized via the monitored data whereby provide decision support for maintenance. Taking nuclear power plants(NPP) as the research object, an HSA framework is proposed based on multi-level integration of equipment, system, sub-function and function in a multi-department collaboration. Due to equipment groups with numerous state parameters, a weighted average fusion operator based on deviation weighting is proposed to fuse equipment-level parameters, which can timely highlight the abnormal equipment. Considering the different amount of data in different health states, an asymmetric multi-class learning method under imbalanced datasets is proposed to fuse the systems’ health values. The self-learning HSA model is established by the information transfer between multiple health levels, so that the health status of sub-functions can be self-perceived and assessed in a timely manner. The health assessors of multiple sub-functions are fused based on ensemble learning to obtain a macro-level operational function HSA decision. Exemplified with the reactivity function of the NPP, the proposed HAS frame is verified effective.

Key words: industrial system, health status assessment, data fusion, nuclear power plant, state-based maintenance

CLC Number:

TG156

JIANG Xiangyu, FENG Yixiong, HONG Zhaoxi, HU Bingtao, SI Hengyuan, TAN Jianrong. Health Status Assessment on the Operation Function of Nuclear Power Plants Based on Multi-level Data Fusion[J]. Journal of Mechanical Engineering, 2024, 60(4): 200-211.

References

[1] 陈俊洵,程龙生,余慧,等.基于EMD-SVD与马田系统的复杂系统健康状态评估[J].系统工程与电子技术,2017,39(7):1542-1548. CHEN Junxun,CHENG Longsheng,YU Hui,et al. Health status assessment for complex systems based on EMD-SVD and Mahalanobis-Taguchi system[J]. Systems Engineering and Electronics,2017,39(7):1542-1548.
[2] 孙强,岳继光.基于不确定性的故障预测方法综述[J].控制与决策,2014,29(5):769-778. SUN Qiang,YUE Jiguang. A review of fault prediction methods based on uncertainty[J]. Control and Decision,2014,29(5):769-778.
[3] ALASWAD S,XIANG Y. A review on condition-based maintenance optimization models for stochastically deteriorating system[J]. Reliability Engineering&System Safety,2017,157:54-63.
[4] 王庆锋,李中,许述剑,等.基于故障案例学习的设备健康评价方法研究[J].机械工程学报,2020,56(20):28-37. WANG Qingfeng,LI Zhong,XU Shujian,et al. Research on equipment health evaluation method based on fault case learning[J]. Journal of Mechanical Engineering,2020,56(20):28-37.
[5] 杨敏.液体火箭发动机试验台健康状态评估方法研究[D].哈尔滨:哈尔滨工业大学,2011. YANG Min. Assessment method research of health condition for liquid rocket engine test facilities[D]. Harbin:Harbin Institute of Technology,2011.
[6] 金飞.核电厂反应性控制功能健康状态评估方法研究[D].哈尔滨:哈尔滨工程大学,2017. JIN Fei. Research on evaluation methods of reactivity control function's health state for nuclear power plant[D]. Harbin:Harbin Engineering University,2017.
[7] 李雪争.核电厂运行健康状态建模与可视化分析[D].天津:天津大学,2017. LI Xuezheng. Modeling for healthy operation state of nuclear power plant and research on visual analysis method[D]. Tianjin:Tianjin University,2017.
[8] HU X,LI S E,YANG Y. Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles[J]. IEEE Transactions on Transportation Electrification,2016,2(2):140-149.
[9] TOLO S,TIAN X,BAUSCH N,et al. Robust on-line diagnosis tool for the early accident detection in nuclear power plants[J]. Reliability Engineering&System Safety,2019,186:110-119.
[10] RAMASSO E,GOURIVEAU R. Remaining useful life estimation by classification of predictions based on a neuro-fuzzy system and theory of belief functions[J]. IEEE Transactions on Reliability,2014,63(2):555-566.
[11] 阮羚,谢齐家,高胜友,等.人工神经网络和信息融合技术在变压器状态评估中的应用[J].高电压技术,2014,40(3):822-828. RUAN Ling,XIE Qijia,GAO Shengyou,et al. Application of artificial neural network and information fusion technology in power transformer condition assessment[J]. High Voltage Engineering,2014,40(3):822-828.
[12] WANG J,SHI J,HE Q. Research on component-level health quantitative assessment based on SVM algorithm for analog electronic circuits[C]//2015 Prognostics and System Health Management Conference (PHM). 2015:1-5.
[13] ZHOU Y,XIAO H,CHEN Z,et al. A dynamic health assessment method for industrial equipment based on SG-FCM clustering algorithm[C]//2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData),2019:34-39.
[14] 张黎明,赵新文,蔡琦.基于SVM的核动力屏蔽泵老化状态评估[J].核动力工程,2011,32(2):124-127. ZHANG Liming,ZHAO Xinwen,CAI Qi. Aging state evaluation of nuclear power shield pump based on SVM[J]. Nuclear Power Engineering,2011,32(2):124-127.
[15] JAVED K,GOURIVEAU R,ZERHOUNI N. A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering[J]. IEEE Transactions on Cybernetics,2015,45(12):2626-2639.
[16] 罗家祥,罗丹,胡跃明.带权重变化和决策融合的ELM在线故障检测[J].控制与决策,2018,33(6):1033-1040. LUO Jiaxiang,LUO Dan,HU Yueming. A new online extreme learning machine with varying weights and decision level fusion for fault detection[J]. Control and Decision,2018,33(6):1033-1040.
[17] 张文博,姬红兵.融合极限学习机[J].电子与信息学报,2013,35(11):2728-2732. ZHANG Wenbo,JI Hongbing. Fusion of extreme learning machines[J]. Journal of Electronics&Information Technology,2013,35(11):2728-2732.
[18] MAHLER R,EBRARY I. Statistical multisource-multitarget information fusion[M]. Boston:Artech House,Inc.,2007.
[19] LI H,HUANG H Z,LI Y F,et al. Physics of failure-based reliability prediction of turbine blades using multi-source information fusion[J]. Applied Soft Computing,2018,72:624-635.
[20] ZHANG Y,YANG Q. An overview of multi-task learning[J]. National Science Review,2018,5(1):30-43.
[21] TAN Z,DE G,LI M,et al. Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine[J]. Journal of Cleaner Production,2020,248:119252.
[22] LEE G,YANG E,HWANG S J. Asymmetric multi-task learning based on task relatedness and loss[C]//BALCAN M F,WEINBERGER K Q. International Conference on Machine Learning,48. San Diego:Jmlr-Journal Machine Learning Research,2016.
[23] XU J,LI L,JI M. Ensemble learning based multi-source information fusion[C]//SU R. 2019 International Conference on Image and Video Processing,and Artificial Intelligence. Shanghai,China:SPIE,2019:81.