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.
Joint Prediction of Remaining Useful Life and Failure Type of Train Wheelsets: Multitask Learning Approach
2018-03-26
Article (Journal)
Electronic Resource
Unknown
APPARATUS AND METHOD FOR PREDICTING REMAINING USEFUL LIFE OF TRAIN
European Patent Office | 2022
|APPARATUS AND METHOD FOR PREDICTING REMAINING USEFUL LIFE OF TRAIN
European Patent Office | 2023
SAGE Publications | 1990
|Experimental and numerical modal analyses of high-speed train wheelsets
Online Contents | 2016
|Experimental and numerical modal analyses of high-speed train wheelsets
SAGE Publications | 2016
|