A hybrid feature learning approach based on convolutional kernels for
  ATM fault prediction using event-log data
Víctor Manuel Vargas Riccardo Rosati César Hervás-Martínez Adriano Mancini Luca Romeo Pedro Antonio Gutiérrez
Predictive Maintenance (PdM) methods aim to facilitate the scheduling of
maintenance work before equipment failure. In this context, detecting early
faults in automated teller machines (ATMs) has become increasingly important
since these machines are susceptible to various types of unpredictable
failures. ATMs track execution status by generating massive event-log data that
collect system messages unrelated to the failure event. Predicting machine
failure based on event logs poses additional challenges, mainly in extracting
features that might represent sequences of events indicating impending
failures. Accordingly, feature learning approaches are currently being used in
PdM, where informative features are learned automatically from minimally
processed sensor data. However, a gap remains to be seen on how these
approaches can be exploited for deriving relevant features from event-log-based
data. To fill this gap, we present a predictive model based on a convolutional
kernel (MiniROCKET and HYDRA) to extract features from the original event-log
data and a linear classifier to classify the sample based on the learned
features. The proposed methodology is applied to a significant real-world
collected dataset. Experimental results demonstrated how one of the proposed
convolutional kernels (i.e. HYDRA) exhibited the best classification
performance (accuracy of 0.759 and AUC of 0.693). In addition, statistical
analysis revealed that the HYDRA and MiniROCKET models significantly overcome
one of the established state-of-the-art approaches in time series
classification (InceptionTime), and three non-temporal ML methods from the
literature. The predictive model was integrated into a container-based decision
support system to support operators in the timely maintenance of ATMs.