  The need for the maintenance of railway track systems have been increasing.
Traditional methods that are currently being used are either inaccurate, labor
and time intensive, or does not enable continuous monitoring of the system. As
a result, in-service train vibrations have been shown to be a cheaper
alternative for monitoring of railway track systems. In this paper, a method is
proposed to detect different maintenance needs of railway track systems using a
single pass of train direction. The DR-Train dataset that is publicly available
was used. Results show that by using a simple classifier such as the k-nearest
neighbor (k-NN) algorithm, the signal energy features of the acceleration data
can achieve 76\% accuracy on two types of maintenance needs, tamping and
surfacing. The results show that the transverse direction is able to more
accurately detect maintenance needs, and triaxial accelerometer can give
further information on the maintenance needs. Furthermore, this paper
demonstrates the use of multi-label classification to detect multiple types of
maintenance needs simultaneously. The results show multi-label classification
performs only slightly worse than the simple binary classification (72\%
accuracy) and that this can be a simple method that can easily be deployed in
areas that have a history of many maintenance issues.
