Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications. (arXiv:2208.13288v1 [cs.LG])

A railway is a complex system comprising multiple infrastructure and rolling
stock assets. To operate the system safely, reliably, and efficiently, the
condition many components needs to be monitored. To automate this process,
data-driven fault detection and diagnostics models can be employed. In
practice, however, the performance of data-driven models can be compromised if
the training dataset is not representative of all possible future conditions.
We propose to approach this problem by learning a feature representation that
is, on the …

applications arxiv detection diagnostics feature learning railway