  The failures of train wheels account for disruptions of train operations and
even a large portion of train derailments. Remaining useful life (RUL) of a
wheelset measures the 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, we propose
a multi-task learning approach to jointly accomplish these two tasks by using a
common input space to achieve more desirable results. We develop a convex
optimization formulation to integrate both least square loss and the negative
maximum likelihood of logistic regression, and model the joint sparsity as the
L2/L1 norm of the model parameters to couple feature selection across tasks.
The experiment results show that our method outperforms the single task
learning method by 3% in prediction accuracy.
