Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Bruin, Tim de; Verbert, K.; Babuška, Robert

doi:10.1109/tnnls.2016.2551940

Cited by 259 publications

(114 citation statements)

References 18 publications

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“…15 Factors not directly related to the track circuit equipment, including rail conductance impairments and ballast degradation, can also cause the system to fail. 13 A large increase in the shunt resistance of the train can also have an effect, 16 although this can be mitigated through the use of additional equipment such as a track circuit assistor. 17 For this investigation, analysis is limited to the EBI Track 200 (also referred to as TI21)-type track circuits, as these were the type of track circuit prevalent in the test areas.…”

Section: Track Circuit Operating Principlesmentioning

confidence: 99%

“…Machine learning techniques show some promise in this area. 13 Further work on the tools to classify the state of the track circuit transmitter will also be carried out. The scoring system presented here is best suited to measuring changes in the amplitude of the track circuit signal.…”

Section: Future Workmentioning

confidence: 99%

“…8 Bruin et al demonstrate similar results using recurrent neural networks. 13 The stochastic nature of this neural network type provides additional resilience by evaluating previous decision data within its current decision cycle. This allows the consideration of time-based analysis within its evaluation of the operating state of the track circuits.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping of the electromagnetic environment on the railway: Condition monitoring of signalling assets

Knight-Percival

Johnson

Richards³

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part F:

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Conventional track circuit condition monitoring systems are fixed at the wayside, with each installation reporting on a single track circuit. In this work, we present a custom-built, sensitive, magnetic field detection system, which can be fitted to the underside of a rail vehicle. With this system installed, some characteristics of an operating track circuit can be monitored from the vehicle whilst it is in motion. By using appropriate analysis techniques, it is possible to identify the signatures of equipment relating to audio frequency track circuits, the topic of this work. Analysing the signatures of track circuit equipment demonstrated that there were clear differences between track circuit assets. By building on other research into the behaviours of failing track circuits, and continuing to conduct this research, the authors believe that it is possible, and beneficial, to perform condition monitoring of track circuits from low-cost equipment mounted on the train. Coupling this with advanced analysis techniques will allow predictive maintenance of track circuits with very little capital outlay.

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Section: Track Circuit Operating Principlesmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Mapping of the electromagnetic environment on the railway: Condition monitoring of signalling assets

Knight-Percival

Johnson

Richards³

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part F:

View full text Add to dashboard Cite

show abstract

“…Recently, with the rapid development of artificial intelligence and deep learning, the majority of methods based on deep neural networks such as DBN, DBM, CNN, and RNN have been broadly used in batch processing and acquired satisfactory results. Nonetheless, a large amount of labelled data needs to be employed to train the networks in order to obtain the desired accuracy.…”

Section: Introductionmentioning

confidence: 99%

Application of fault monitoring and diagnosis in process industry based on fourth order moment and singular value decomposition

et al. 2019

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Principal component analysis (PCA) and partial least squares (PLS) have been frequently used for process industry monitoring; however, their application on industrial sites is limited because they cannot be used to process data with non-Gaussian distribution. Independent component analysis (ICA) has become a powerful modelling method for non-Gaussian process monitoring.However, the ICA-based modelling method has been found to contribute to double the amount of data loss in feature extraction. There are two reasons for this. First, when the PCA algorithm is used to whiten the original data, the smaller principal component is discarded. Second, when selecting independent components, some smaller independent components will be discarded according to the evaluation index. The abovementioned two data feature extraction methods may discard useful information for fault monitoring, which will inevitably lead to inaccurate fault monitoring. To solve this problem, a fault monitoring and diagnosis method based on fourth order moment (FOM) analysis and singular value decomposition (SVD) is proposed. First, the fourth order moments of each process variable were constructed separately. Then, the data space of the fourth order moments was decomposed by singular value decomposition to establish the global monitoring statistics. Finally, the contribution diagram was drawn and the fault diagnosis was performed based on the global monitoring results. The proposed method was applied to the Tennessee Eastman (TE) simulation platform, and its effectiveness and feasibility were verified by a comparison with PCA and ICA. K E Y W O R D Sfault diagnosis, fourth order moment, process monitoring, singular value decomposition

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“…In contrast to standard RNN, LSTM is amenable to overcome the problem of long-term dependencies. Bruin [21] put forward to apply LSTM neural network to fault identification of track circuits. However, the dependencies of time series make it difficult to use LSTM Complexity 3 for parallel computation.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

et al. 2018

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Timely detection and efficient recognition of fault are challenging for the bogie of high-speed train (HST), owing to the fact that different types of fault signals have similar characteristics in the same frequency range. Notice that convolutional neural networks (CNNs) are powerful in extracting high-level local features and that recurrent neural networks (RNNs) are capable of learning long-term context dependencies in vibration signals. In this paper, by combining CNN and RNN, a so-called convolutional recurrent neural network (CRNN) is proposed to diagnose various faults of the HST bogie, where the capabilities of CNN and RNN are inherited simultaneously. Within the novel architecture, the proposed CRNN first filters out the features from the original data through convolutional layers. Then, four recurrent layers with simple recurrent cell are used to model the context information in the extracted features. By comparing the performance of the presented CRNN with CNN, RNN, and ensemble learning, experimental results show that CRNN achieves not only the best performance with accuracy of 97.8% but also the least time spent in training model.

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Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Cited by 259 publications

References 18 publications

Mapping of the electromagnetic environment on the railway: Condition monitoring of signalling assets

Mapping of the electromagnetic environment on the railway: Condition monitoring of signalling assets

Application of fault monitoring and diagnosis in process industry based on fourth order moment and singular value decomposition

Convolutional Recurrent Neural Network for Fault Diagnosis of High‐Speed Train Bogie

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