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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