Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems

Chen, Jian; Roberts, Clive; Weston, Paul

doi:10.1016/j.conengprac.2007.06.007

Cited by 120 publications

(63 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A distinction can be made between methods that use data collected by a measurement train [6], [7], [9], [10] and methods that use data collected via track-side monitoring devices [1], [2], [8]. In this work, track-side monitoring devices are considered because they continuously monitor the system health and are therefore suitable for the early diagnosis of faults.…”

Section: Related Workmentioning

confidence: 99%

“…Several methods for fault diagnosis in railway track circuits have been proposed in literature [1], [2], [6]- [10]. A distinction can be made between methods that use data collected by a measurement train [6], [7], [9], [10] and methods that use data collected via track-side monitoring devices [1], [2], [8].…”

Section: Related Workmentioning

confidence: 99%

“…A S railway networks are becoming busier, they are required to operate with increasing levels of availability and reliability [1]. To enable the safe operation of a railway network, it is crucial to detect the presence of trains in the sections of a railway track.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Bruin

Verbert

Babuška

2017

IEEE Trans. Neural Netw. Learning Syst.

259

View full text Add to dashboard Cite

Abstract-Timely detection and identification of faults in railway track circuits is crucial for the safety and availability of railway networks. In this paper, the use of the Long Short Term Memory Recurrent Neural Network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from multiple track circuits in a geographic area, faults are diagnosed from their spatial and temporal dependencies. A generative model is used to show that the LSTM network can learn these dependencies directly from the data. The network correctly classifies 99.7% of the test input sequences, with no false positive fault detections. Additionally, the t-SNE method is used to examine the resulting network, further showing that it has learned the relevant dependencies in the data. Finally, we compare our LSTM network to a convolutional network trained on the same task. From this comparison we conclude that the LSTM network architecture better suited for the railway track circuit fault detection and identification tasks than the convolutional network.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Bruin

Verbert

Babuška

2017

IEEE Trans. Neural Netw. Learning Syst.

259

View full text Add to dashboard Cite

show abstract

“…Therefore, the application based on fuzzy logic theory in fault diagnostics is closer to human thinking habits and language expression. Fuzzy logic is an effective pattern recognition method, which has been successfully used in power [43], transmission line [44], transportation [45], and industrial production [46]. Fuzzy logic mainly imitates human's logical thinking and thus has strong capability of expressing knowledge.…”

Section: Fuzzy Logic and Neuro-fuzzy Systems (Nfss)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

show abstract

“…Over 12,000 track circuit failures were reported in the UK during [2004][2005], resulting in 1.5 million minutes of attributable delay. Typically, the UK railway infrastructure manager can be penalised with a penalty of £20-60 per delay minute arising from infrastructure failure [1]. Therefore, the ability to detect and diagnose track circuit failures in order to provide a fast response to failures/incidents has significant economic benefits.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis of railway track circuits

Patra

Kumar

2010

Proceedings of the Institution of Mechanical Engineers, Part F:

View full text Add to dashboard Cite

Railways are expected to operate with ever increasing availability. The availability of railway systems and subsystems influences the overall operational availability. As the track circuit is a key component of railway signalling and control, it can contribute significantly to the loss of availability of the railway system. One way to increase the availability of the track circuit is through implementing proper a maintenance policy. In this article, Petri-Nets have been used to develop models for availability analysis of the track circuit for both time-based and condition based maintenance. A sensitivity analysis has been performed to estimate the effect of maintenance parameters on availability performance using cost-benefit analysis.

show abstract

Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems

Cited by 120 publications

References 10 publications

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Availability analysis of railway track circuits

Contact Info

Product

Resources

About