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Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: Multitask Learning Approach
| 题名： | Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: Multitask Learning Approach |
| 其他题名： | Askarinejad,H.,Dhanasekar,M.,and Cole,C.(2013)."Assessing the effects of track input on the response of insulated rail joints using field experiments."Proc.Inst.Mech.Eng.Part F,227(2),176-187. |
| 正文语种： | 英文 |
| 作者： | Weixin Wang |
| 关键词： | prediction；learning；formulation；optimization；failures；integrate；parameter；task；joint；operation |
| 摘要： | Train wheel failures account for disruptions of train operations and even a large portion of train derailments. Remaining useful life (RUL) of a wheelset measures how soon the next failure will arrive, and the failure type reveals how severe the failure will be. RUL prediction is a regression task, whereas failure type is a classification task. In this paper, the authors propose a multitask learning approach to jointly accomplish these two tasks by using a common input space to achieve more desirable results. A convex optimization formulation is developed to integrate least-squares loss and negative maximum likelihood of logistic regression as well as model the joint sparsity as the L2/L1 norm of the model parameters to couple feature selection across tasks. The experiment results show that the multitask learning method outperforms both the single-task learning method and Random Forest. |
| 出版年： | 2018 |
| 论文唯一标识： | P-72Y2018V144N06002 |
| 英文栏目名称： | TECHNICAL PAPERS |
| doi： | 10.1061/JTEPBS.0000113 |
| 期刊名称： | Journal of Transportation Engineering |
| 拼音刊名(出版物代码)： | P-72 |
| 卷： | 144 |
| 期： | 06 |
| 页码： | 1-11 |
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