云边协同下时空特征融合的轴承剩余寿命预测
Spatio-Temporal Feature Fusion for Residual Life Prediction of Bearings Under Cloud-Edge Collaboration
潘隆基 1唐向红 1陆见光 2刘方杰 3刘汝迪4
作者信息
- 1. 贵州大学公共大数据国家重点实验室,贵阳 550025
- 2. 贵州大学公共大数据国家重点实验室,贵阳 550025;重庆工业大数据创新中心有限公司,重庆 400707
- 3. 贵州新思维科技有限责任公司,贵阳 550001
- 4. 贵州大学现代制造技术教育部重点实验室,贵阳 550025
- 折叠
摘要
为了解决滚动轴承剩余使用寿命(remaining useful life,RUL)预测中的特征依赖关系、表征退化趋势和实时性问题,提出了一种云边协同下时空特征融合的轴承剩余寿命预测方法.首先,在离线阶段依据专家先验知识对轴承历史退化数据进行去噪处理;其次,对去噪信号进行时域与频域退化特征的提取,并对提取的退化特征进行分析与筛选;最后,采用皮尔逊相关系数对选取的退化特征进行相似相关性分析,并根据相似相关参数构建特征空间图作为图卷积网络(graph convolutional network,GCN)-Transformer模型输入以进行训练,并在云边协同实时预测阶段测试以减轻云端负担.在XJTU-SY数据集上的实验中,所提方法与其他文献预测方法相比在MAE与RMSE指标上分别降低了10.5%与11.3%,在平均响应时间(实时性指标)上降低到采用云计算策略的0.363.实验结果验证了所提方法的有效性.
Abstract
In order to address the issues of feature dependencies,degradation trend representation,and real-time concerns in the prediction of remaining useful life(RUL)for rolling bearings,this paper introduces a method for predicting the remaining life of bearings that integrates spatial and temporal features in a cloud-edge collaborative framework.Firstly,in the offline phase,noise reduction is performed on historical degra-dation data of bearings based on expert prior knowledge.Secondly,temporal and frequency domain degra-dation features are extracted from the denoised signals,and the extracted degradation features are analyzed and screened.Finally,in order to better capture the interaction between features,the method uses the Pear-son correlation coefficient to analyze the similarity correlation of the selected degradation features,and con-structs a feature space map based on the similarity correlation parameter as an input to the graph convolu-tional network(GCN)-Transformer model for training and testing in the cloud-side collaboration.The real-time prediction phase is tested to reduce the burden on the cloud to improve the real-time prediction.In the experiments on the XJTU-SY dataset,the proposed method reduces the MAE and RMSE metrics by 10.5%and 11.3%,respectively,and reduces the average response time(real-time metrics)to 0.363 using the cloud computing strategy compared with other literature prediction methods.The experimental results vali-date the effectiveness of the proposed method.
关键词
滚动轴承/剩余使用寿命/时空特征融合/云边协同/图卷积网络Key words
rolling bearings/remaining useful life/spatio-temporal feature integration/cloud-edge collabo-ration/graph convolutional networks引用本文复制引用
基金项目
黔科合基础项目(271/QKHJC-ZK[2021]YB271)
黔科合支撑项目(074/QKHZC[2022]YB074)
出版年
2024
NETL
NSTL
万方数据