An Improved Track Circuit Fault Prediction Method Based on Attention Mechanism and Data Adaptation
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.
National Natural Science Foundation of China under Grants 61833001.
Email Address of Submitting Author20120233@bjtu.edu.cn
ORCID of Submitting Author0000-0002-7977-4505
Submitting Author's InstitutionSchool of Electronics and Information Engineering, Beijing Jiaotong University
Submitting Author's Country