| 304 | 0 | 98 |
| Downloads | Citas | Reads |
To solve the problems of high computational cost and error accumulation in the field of online remaining useful life(RUL) prediction of rotating machinery with unknown working conditions, a new online RUL prediction method was proposed based on unsupervised deep domain-adversarial adaptation. Firstly, by employing offline degradation data and online early fault data, a deep domain-adversarial network was constructed as a pre-trained model. Secondly, the pseudo-labels of online sequential data blocks were obtained by feeding them into the regression predictor of the pre-trained model that was re-domain adaptation. Finally, the structure and parameters of the pre-trained model were transferred to a target model, and by freezing some parameters of the target model, the remaining parameters were fine-tuned with the online data block and its pseudo-labels to achieve the online RUL prediction. Experimental results on the IEEE PHM Challenge 2012 bearing dataset demonstrated that the proposed method could sequentially and accurately predict the RUL values of online bearing, which provided a practical solution for bearing RUL prediction in online scenarios.
[1] 刘晶，宁森，徐伟杰，等.基于多尺度残差子域适应的轴承故障诊断方法[J].郑州大学学报(理学版),2023,55(5):39-46.LIU J,NING S,XU W J,et al.Bearing fault diagnosis method based on multi-scale residual sub-domain adaptation[J].Journal of Zhengzhou university (natural science edition),2023,55(5):39-46.
[2] WANG B,LEI Y G,LI N P,et al.A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J].IEEE transactions on reliability,2020,69(1):401-412.
[3] KUMAR P S,KUMARASWAMIDHAS L A,LAHA S K.Selection of efficient degradation features for rolling element bearing prognosis using Gaussian process regression method[J].ISA transactions,2021,112:386-401.
[4] DEUTSCH J,HE D.Using deep learning-based approach to predict remaining useful life of rotating components[J].IEEE transactions on systems,man,and cybernetics:systems,2018,48(1):11-20.
[5] ZHU J,CHEN N,SHEN C Q.A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions[J].Mechanical systems and signal processing,2020,139:106602.
[6] MAO W T,LIU J,CHEN J X,et al.An interpretable deep transfer learning-based remaining useful life prediction approach for bearings with selective degradation knowledge fusion[J].IEEE transactions on instrumentation and measurement,2022,71:1-16.
[7] DING Y F,JIA M P,MIAO Q H,et al.Remaining useful life estimation using deep metric transfer learning for kernel regression[J].Reliability engineering & system safety,2021,212:107583.
[8] COSTA P R,AK?AY A,ZHANG Y Q,et al.Remaining useful lifetime prediction via deep domain adaptation[J].Reliability engineering & system safety,2020,195:106682.
[9] 陈佳鲜，毛文涛，刘京，等.基于时间序列迁移递归预测的未知工况下滚动轴承在线剩余寿命评估[J].控制与决策，2023,38(1):112-122.CHEN J X,MAO W T,LIU J,et al.Online remaining useful life estimation of bearing under unknown working conditions based on time series transfer recursive prediction[J].Control and decision,2023,38(1):112-122.
[10] ZENG F C,LI Y M,JIANG Y H,et al.An online transfer learning-based remaining useful life prediction method of ball bearings[J].Measurement,2021,176:109201.
[11] MAO W T,HE J L,ZUO M J.Predicting remaining useful life of rolling bearings based on deep feature representation and transfer learning[J].IEEE transactions on instrumentation and measurement,2020,69(4):1594-1608.
[12] NECTOUX P,GOURIVEAU R,MEDJAHER K,et al.Pronostia:an experimental platform for bearings accelerated life test[C]//IEEE International Conference on Prognostics and Health Management.Piscataway:IEEE Press,2010:1-8.
[13] MAO W T,HE J L,TANG J M,et al.Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network[J].Advances in mechanical engineering,2018,10(12):1-8.
[14] HUANG J Y,SMOLA A J,GRETTON A,et al.Correcting sample selection bias by unlabeled data[M]//Advances in Neural Information Processing Systems 19.Cambridge:MIT Press,2007:601-608.
[15] FERNANDO B,HABRARD A,SEBBAN M,et al.Unsupervised visual domain adaptation using subspace alignment[C]//IEEE International Conference on Computer Vision.Piscataway:IEEE Press,2014:2960-2967.
Basic Information:
DOI：10.13705/j.issn.1671-6841.2023132
China Classification Code:TH17;TP18
Citation Information:
[1]LIU Keying,ZHANG Yanna,MAO Wentao ,et al.Online Remaining Useful Life Prediction Method Based on Unsupervised Deep Domain-adversarial Adaptation[J].Journal of Zhengzhou University(Natural Science Edition),2025,57(01):81-87.DOI:10.13705/j.issn.1671-6841.2023132.
Fund Information:
国家自然科学基金项目(U1704158); 河南省自然科学基金项目(232300421390); 河南省高等学校重点科研项目(23A510003)
2024-04-29
2024-04-29
2024-04-29