Fault Prediction of Turnout Equipment Based on Double-layer Gated Recurrent Unit Neural Network

Shi, Xianqing; Dai, Shenghua

doi:10.1109/itsc48978.2021.9564437

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…6 Shi et al make research on switch equipment fault prediction method based on Gated Recurrent Unit (GRU). 7 Dai et al make the fault prediction study based on data provided by a major rail transit agency in the United States that can identified around one-third of signal failures one month in advance by concentrating on 10% of locations on the network. 8 Although DL models can achieve fault prediction of track circuit, it usually necessitates a substantial quantity of labelled data for training, which frequently requires a lengthy period and extensive resources for the collection and labelling of data.…”

Section: Introductionmentioning

confidence: 99%

A Simple Heterogeneous Transfer Learning Method for Track Circuit Fault Prediction

Na,

Cai,

Zhang

et al. 2024

Preprint

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Section: Introductionmentioning

confidence: 99%

A Simple Heterogeneous Transfer Learning Method for Track Circuit Fault Prediction

Na,

Cai,

Zhang

et al. 2024

Preprint

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“…Kang et al proposed a method of forecasting of track circuit failure compensation capacitor number based on long short-term memory network (LSTM) [5]. Shi et al proposed a turnout equipment fault prediction method based on gated recurrent unit neural network (GRU) [9]. In this research, a new track circuit fault prediction model based on bidirectional LSTM neural network and multi-head attention mechanism is proposed to anticipate future failures in track circuit.…”

Section: Introductionmentioning

confidence: 99%

An Improved Track Circuit Fault Prediction Method Based on Attention Mechanism and Data Adaptation

Na¹,

Dai²,

Li³

et al. 2022

Preprint

Self Cite

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<p>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.</p>

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Data-driven technology of fault diagnosis in railway point machines: review and challenges

Cao

Tang

et al. 2022

Transportation Safety and Environment

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Safety and reliability are absolutely vital for sophisticated Railway Point Machines (RPMs). Hence, various kinds of sensors and transducers are deployed on RPMs as much as possible to monitor their behaviour for detection of incipient faults and anticipation using data-driven technology. This paper firstly analyses and summarizes six RPMs’ characteristics and then reviews the data-driven algorithms applied to fault diagnosis in RPMs during the past decade. It provides not only the process and evaluation metrics but also the pros and cons of these different methods. Ultimately, regarding the characteristics of RPMs and the existing studies, eight challenging problems and promising research directions are pointed out.

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Fault Prediction of Turnout Equipment Based on Double-layer Gated Recurrent Unit Neural Network

Cited by 5 publications

References 5 publications

A Simple Heterogeneous Transfer Learning Method for Track Circuit Fault Prediction

A Simple Heterogeneous Transfer Learning Method for Track Circuit Fault Prediction

An Improved Track Circuit Fault Prediction Method Based on Attention Mechanism and Data Adaptation

Data-driven technology of fault diagnosis in railway point machines: review and challenges

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