摘要
轴承作为众多生产设备中重要的部件之一,对其剩余使用寿命的研究有较大的价值。文中针对传统轴承剩余使用寿命预测中未充分考虑不同环境下的衰减状态变化和时序相关性而导致的预测误差大的问题,提出了一种基于时空双细胞状态自适应网络(ST-DCSN)的轴承剩余使用寿命预测方法。采用一种时间状态和空间状态并存的内嵌卷积操作双状态循环网络,并引入时空双细胞状态和子细胞状态差分机制,实现对轴承衰减状态的自适应感知。该方法在时间和空间维度上对轴承监测数据进行特征状态有效捕捉,从而解决轴承剩余使用寿命预测中环境和时序问题对预测性能的影响。为了探究文中所提方法的有效性以及对比其他近年优秀方法的优越性,采用两个真实的轴承寿命加速衰减实验数据集FEMTO-ST和XJTU-SY对文中所提方法进行了验证,分别进行了消融实验和对比实验,并以4种指标对预测性能进行评价。消融实验结果表明,相比去除空间状态细胞和去除动静态子细胞的组别,完全版本的ST-DCSN能够得到更平稳以及性能指标更好的预测结果。对比其他方法,文中所提方法能够得到更优秀的预测性能,体现在拟合性更高以及轴承寿命末期预测结果的平稳性更好,证明了ST-DCSN方法能有效提高轴承剩余使用寿命预测的准确性。
Bearing is one of the important components in many production equipment,and the study of its remaining useful life is of great value.A prediction method for remaining useful life of bearings based on spatialtemporal dual-cell state self-adaptive network(ST-DCSN)was proposed for the prediction error caused by the degradation state change and timing correlation that are not fully considered in the traditional bearing remaining life prediction in different environments.This paper adopted an embedded convolution of coexisting temporal and spatial states to operate the dual-state recurrent network and introduced spatio-temporal dual-cell state and sub-cell state differential mechanism to realize adaptive perception of bearing attenuation states.This method effectively captures the feature state of the bearing monitoring data in both temporal and spatial dimensions,so as to solve the influence of environmental and timing problems on the prediction performance of bearing remaining life prediction.To investigate the effectiveness of the proposed method and compare it with other state-of-the-art approaches,two real bearing life accelerated degradation datasets,namely FEMTO-ST and XJTU-SY,were used for validation.Both ablation experiments and comparative experiments were conducted,and four evaluation metrics were employed to assess the prediction performance.The ablation results demonstrate that the complete version of ST-DCSN outperforms the experiment groups with removed spatial cell and dynamic and static sub-cell in terms of stability and performance metrics.Compared to other methods,the proposed method achieves superior prediction performance with higher fitness and better stability in the prediction results at the end of life of bearing.This demonstrates that the ST-DCSN method can effectively improve the accuracy of bearing’s remaining useful life prediction.
作者
李方
郭炜森
张平
罗龙
LI Fang;GUO Weisen;ZHANG Ping;LUO Long(School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,Guangdong,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2023年第9期69-81,共13页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省自然科学基金资助项目(2021A1515012126)
广州市科技计划项目(202206030008)
国家重点研发计划项目(2018YFB1700500)
大数据与智能机器人教育部重点实验室开放课题基金资助项目(202101)。
关键词
时空双细胞
自适应网络
剩余使用寿命预测
轴承
深度学习
spatial-temporal dual cell
adaptive network
remaining useful life prediction
bearing
deep learning
