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New methods for the condition monitoring of level crossings

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  • Fausto Pedro García Márquez
  • Diego J. Pedregal
  • Clive Roberts

Abstract

Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a ‘normal’ or ‘expected’ shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque–Bera and Ljung–Box have been considered for testing the model.

Suggested Citation

  • Fausto Pedro García Márquez & Diego J. Pedregal & Clive Roberts, 2015. "New methods for the condition monitoring of level crossings," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(5), pages 878-884, April.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:5:p:878-884
    DOI: 10.1080/00207721.2013.801090
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    References listed on IDEAS

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    1. Pedregal, Diego J. & Carmen Carnero, Ma, 2006. "State space models for condition monitoring: a case study," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 171-180.
    2. Christer, A. H. & Wang, W. & Sharp, J. M., 1997. "A state space condition monitoring model for furnace erosion prediction and replacement," European Journal of Operational Research, Elsevier, vol. 101(1), pages 1-14, August.
    3. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    4. García Márquez, Fausto Pedro & Schmid, Felix, 2007. "A digital filter-based approach to the remote condition monitoring of railway turnouts," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 830-840.
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    Cited by:

    1. Arcos Jiménez, Alfredo & Zhang, Long & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2020. "Maintenance management based on Machine Learning and nonlinear features in wind turbines," Renewable Energy, Elsevier, vol. 146(C), pages 316-328.
    2. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    3. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    4. Carlos Quiterio Gómez Muñoz & Fausto Pedro García Márquez, 2016. "A New Fault Location Approach for Acoustic Emission Techniques in Wind Turbines," Energies, MDPI, vol. 9(1), pages 1-14, January.
    5. Aleksandar Blagojević & Sandra Kasalica & Željko Stević & Goran Tričković & Vesna Pavelkić, 2021. "Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model," Sustainability, MDPI, vol. 13(2), pages 1-20, January.

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