  Railway points are among the key components of railway infrastructure. As a
part of signal equipment, points control the routes of trains at railway
junctions, having a significant impact on the reliability, capacity, and
punctuality of rail transport. Traditionally, maintenance of points is based on
a fixed time interval or raised after the equipment failures. Instead, it would
be of great value if we could forecast points' failures and take action
beforehand, minimising any negative effect. To date, most of the existing
prediction methods are either lab-based or relying on specially installed
sensors which makes them infeasible for large-scale implementation. Besides,
they often use data from only one source. We, therefore, explore a new way that
integrates multi-source data which are ready to hand to fulfil this task. We
conducted our case study based on Sydney Trains rail network which is an
extensive network of passenger and freight railways. Unfortunately, the
real-world data are usually incomplete due to various reasons, e.g., faults in
the database, operational errors or transmission faults. Besides, railway
points differ in their locations, types and some other properties, which means
it is hard to use a unified model to predict their failures. Aiming at this
challenging task, we firstly constructed a dataset from multiple sources and
selected key features with the help of domain experts. In this paper, we
formulate our prediction task as a multiple kernel learning problem with
missing kernels. We present a robust multiple kernel learning algorithm for
predicting points failures. Our model takes into account the missing pattern of
data as well as the inherent variance on different sets of railway points.
Extensive experiments demonstrate the superiority of our algorithm compared
with other state-of-the-art methods.
