An Improved Track Circuit Fault Prediction Method Based on Attention
Mechanism and Data Adaptation
Abstract
Track circuit is one of the most important parts of train operation
control system. In current railway transportation research area, track
circuit intelligent maintenance, including fault diagnosis and
prediction is mainly achieved by using deep learning (DL). However, the
application of DL models in practical has a great limitation. The
training of track circuit intelligent maintenance DL models requires
huge amount of labeled data. And DL models are too fragile since real
data and training data can hardly meet the requirement of independent
and identically distributed. In this paper, a new track circuit fault
prediction model based on long short-term memory network and attention
mechanism is proposed. To enhance the practical performance of this DL
model, a deep data adaptation algorithm is applied to make an improved
method to overcome degradation problem caused by non-identically
distributed of training and real data. Moreover, it can be also applied
to reduce data dependence of the DL model training. To evaluate the
performance of the proposed DL model and the improved method,
experiments were carried out based on real data from China Railway
Shenyang Group Company Limited computer monitoring system. The
experimental results show that the proposed track circuit fault
prediction model can provide fault prediction results with an accuracy
better than 95%, the improved method has a better performance than the
DL model without deep data adaptation algorithms, and the proposed deep
data adaptation algorithm has a better performance than conventional
data adaptation algorithms. Compared with the conventional fault
prediction DL model without transfer learning, the proposed method can
achieve a much better performance, with an improvement, in the test, of
over 75%. And the proposed deep data adaptation algorithm can make an
accuracy improvement, in the test, of over 30% than conventional data
adaptation methods.