Evolvable Remaining Useful Life Estimation of Nuclear Power Equipment Under Human-cyber-physical Collaboration

doi:10.3901/JME.2025.04.302

Abstract

Abstract: In the new era of Industry 5.0, the integration of physical entities of equipment with human cognition and information technology is an important way to promote the intelligent of prognostics and health management(PHM). The estimation of residual life is a key link in PHM. It is the basis for the predictive maintenance of equipment. Inspired by the human memory and forgetting mechanism, an evolvable model framework of human-cyber-physical collaboration for remaining useful life estimation is proposed, which can simultaneously predict the continuous state and discrete state of the equipment. The model adopts instance-based learning to absorb and retain knowledge from time series data without assuming a fixed failure threshold and degradation stage in advance. The model aims to gradually adjust and become stable with incremental samples, which is suitable for the equipment with small-scale samples and insufficient prior knowledge, or the equipment in a changing operating environment. The kernel least mean square (KLMS) algorithm is used adapted as the underlying algorithm so that the model structure and parameters could be updated online. After KLMS expands the input space into a feature space, the nearest-instance centroid estimation(NICE) is used to improve it by automatically dividing the feature space into different sub-regions with the input data learning, to realize the prediction of future signals and obtain health status information at the same time. Then the remaining useful life is derived. In order to make the model network more compact, the online modified vector quantization(M-VQ) method is introduced to reduce the computational complexity by eliminating redundant basis functions of the model. The proposed model framework is applied to estimate the remaining life of the feed pump in a pressurized water reactor, and the effectiveness of the method is verified.

Key words: human-cyber-physical collaboration, remaining useful life, synchronous prediction, kernel least mean square

CLC Number:

TG156

JIANG Xiangyu, FENG Yixiong, ZHANG Zhifeng, SONG Xiuju, HONG Zhaoxi, HU Bingtao, TAN Jianrong. Evolvable Remaining Useful Life Estimation of Nuclear Power Equipment Under Human-cyber-physical Collaboration[J]. Journal of Mechanical Engineering, 2025, 61(4): 302-313.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.2025.04.302

http://www.cjmenet.com.cn/EN/Y2025/V61/I4/302

References

[1] GUO Jian，LI Zhaojun，LI Meiyan. A review on prognostics methods for engineering systems[J]. IEEE Transactions on Reliability，2020，69(3):1110-1129.
[2] 李乃鹏，蔡潇，雷亚国，等. 一种融合多传感器数据的数模联动机械剩余寿命预测方法[J]. 机械工程学报，2021，57(20):29-37，46. LI Naipeng，CAI Xiao，LEI Yaguo，et al. A model-data-fusion remaining useful life prediction method with multi-sensor fusion for machinery[J]. Journal of Mechanical Engineering，2021，57(20):29-37，46.
[3] SOLEIMANI M，FAIZ J，NASAB P S，et al. Temperature measuring-based decision-making prognostic approach in electric power transformers winding failures[J]. IEEE Transactions on Instrumentation and Measurement，2020，69(9):6995-7003.
[4] HU Changhua，PEI Hong，SI Xiaosheng，et al. A prognostic model based on DBN and diffusion process for degrading bearing[J]. IEEE Transactions on Industrial Electronics，2020，67(10):8767-8777.
[5] LING M H，NG H K T，TSUI K L. Bayesian and likelihood inferences on remaining useful life in two-phase degradation models under gamma process[J]. Reliability Engineering & System Safety，2019，184:77-85.
[6] MA Qiuhui，ZHENG Ying，YANG Weidong，et al. Remaining useful life prediction of lithium battery based on capacity regeneration point detection[J]. Energy，2021，234:121233.
[7] ZHANG Yixing，FENG Fei，WANG Shunli，et al. Joint nonlinear-drift-driven Wiener process-Markov chain degradation switching model for adaptive online predicting lithium-ion battery remaining useful life[J]. Applied Energy，2023，341:121043.
[8] LIU Zhenyu，LIU Hui，JIA Weiqiang，et al. A multi-head neural network with unsymmetrical constraints for remaining useful life prediction[J]. Advanced Engineering Informatics，2021，50:101396.
[9] ZHANG Yuhan，YANG Ying，LI He，et al. A data-driven modeling method for stochastic nonlinear degradation process with application to RUL estimation[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2022，52(6):3847-3858.
[10] HU Kui，CHENG Yiwei，WU Jun，et al. Deep bidirectional recurrent neural networks ensemble for remaining useful life prediction of aircraft engine[J]. IEEE Transactions on Cybernetics，2023，53(4):2531-2543.
[11] PRAKASH O，SAMANTARAY A K. Prognosis of dynamical system components with varying degradation patterns using model-data-fusion[J]. Reliability Engineering & System Safety，2021，213:107683.
[12] AL-DULAIMI A，ZABIHI S，ASIF A，et al. A multimodal and hybrid deep neural network model for remaining useful life estimation[J]. Computers in Industry，2019，108:186-196.
[13] LI Zhixiong，WU Dazhong，HU Chao，et al. An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction[J]. Reliability Engineering & System Safety，2019，184:110-122.
[14] XIA Pengcheng，HUANG Yixiang，LI Peng，et al. Fault knowledge transfer assisted ensemble method for remaining useful life prediction[J]. IEEE Transactions on Industrial Informatics，2022，18(3):1758-1769.
[15] JIANG Xiangyu，FENG Yixiong，LI Zhiwu，et al. Hierarchical data fusion-based health state assessment of nuclear power plant operation under human-cyber-physical system architecture[J]. IEEE Transactions on Automation Science and Engineering，2023，22:523-533.
[16] FENG Yixiong，JIANG Xiangyu，HONG Zhaoxi，et al. Human-cyber-physical system for operation in nuclear reactor possessing asymmetric multi-task learning-based predicting framework[J]. Journal of Manufacturing Systems，2022，64:443-453.
[17] YANG Lingxiao，SUN Qiuye，ZHANG Ning，et al. Optimal energy operation strategy for we-energy of energy internet based on hybrid reinforcement learning with human-in-the-loop[J]. IEEE Transactions on Systems，Man，and Cybernetics:Systems，2022，52(1):32-42.
[18] CHEN Xiao，EDER M A，SHIHAVUDDIN A，et al. A human-cyber-physical system toward intelligent wind turbine operation and maintenance[J]. Sustainability，2021，13(2):561.
[19] ZHENG Zhong，ZHANG Kaitian，GAO Xiaoqiang. Human-cyber-physical system for production and operation decision optimization in smart steel plants[J]. Science China Technological Sciences，2022，65(2):247-260.
[20] LIU Jie，ZIO E. An adaptive online learning approach for support vector regression:Online-SVR-FID[J]. Mechanical Systems and Signal Processing，2016，76-77:796-809.
[21] LI Xifeng，PENG Libiao，GAO Le，et al. A robust hybrid filtering method for accurate battery remaining useful life prediction[J]. IEEE Access，2019，7:57843-57856.
[22] ZHOU Haowen，HUANG Jinquan，LU Feng，et al. Echo state kernel recursive least squares algorithm for machine condition prediction[J]. Mechanical Systems and Signal Processing，2018，111:68-86.
[23] HU Jiawen，SUN Qiuzhaung，YE Zhisheng，et al. Joint modeling of degradation and lifetime data for rul prediction of deteriorating products[J]. IEEE Transactions on Industrial Informatics，2021，17(7):4521-4531.
[24] MIAO Huihui，LI Bing，SUN Chuang，et al. Joint learning of degradation assessment and RUL prediction for aeroengines via dual-task deep LSTM networks[J]. IEEE Transactions on Industrial Informatics，2019，15(9):5023-5032.
[25] ZHOU Haowen，HUANG Jinquan，LU Feng. Reduced kernel recursive least squares algorithm for aero-engine degradation prediction[J]. Mechanical Systems and Signal Processing，2017，95:446-467.
[26] BAO Rongjing，RONG Haijun，YANG Zhixin，et al. A novel prognostic approach for RUL estimation with evolving joint prediction of continuous and discrete states[J]. IEEE Transactions on Industrial Informatics，2019，15(9):5089-5098.
[27] PARISI G I，KEMKER R，PART J L，et al. Continual lifelong learning with neural networks:A review[J]. Neural Networks:The Official Journal of the International Neural Network Society，2019，113:54-71.
[28] 庞业勇，王少军，彭宇，等. 一种在线时间序列预测的核自适应滤波器向量处理器[J]. 电子与信息学报，2016，38(1):53-62. PANG Yeyong，WANG Shaojun，PENG Yu，et al. A kernel adaptive filter vector processor for online time series prediction[J]. Journal of Electronics & Information Technology，2016，38(1):53-62.
[29] 韩敏，马俊珠，任伟杰，等. 基于核自适应滤波器的时间序列在线预测研究综述[J]. 自动化学报，2021，47(4):730-746. HAN Min，MA Junzhu，REN Weijie，et al. A survey of time series online prediction based on kernel adaptive filters[J]. Acta Automatica Sinica，2021，47(4):730-746.