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Fault tolerant design of a field data modular readout architecture for railway applications

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  • Fort, Ada
  • Mugnaini, Marco
  • Vignoli, Valerio
  • Gaggii, Vittorio
  • Pieralli, Moreno

Abstract

Modern data acquisition systems used to collect sensor signals are usually designed taking into consideration performance and operating parameters which are mainly related to sensitivity, selectivity, resolution and stability over time. In addition to such important features, field application systems should also respond to other constraints like reliability and availability and additionally, depending on the specific application, to some peculiar requirements in terms of safety. The present paper is addressed to supply an overview of the implications, during a sensor input/output hardware module design, of such parameters as the safety integrity level. The discussion involves the overall system design once integrated with availability considerations. In this manuscript, considerations concerning the on board software implementation are omitted without loss in generality. The study has been developed taking into account solutions suitable for railway applications like signaling or crossing detection systems.

Suggested Citation

  • Fort, Ada & Mugnaini, Marco & Vignoli, Valerio & Gaggii, Vittorio & Pieralli, Moreno, 2015. "Fault tolerant design of a field data modular readout architecture for railway applications," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 456-462.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:456-462
    DOI: 10.1016/j.ress.2015.06.008
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    References listed on IDEAS

    as
    1. Fort, A. & Mugnaini, M. & Vignoli, V., 2015. "Hidden Markov Models approach used for life parameters estimations," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 85-91.
    2. Ding, Long & Wang, Hong & Kang, Kai & Wang, Kai, 2014. "A novel method for SIL verification based on system degradation using reliability block diagram," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 36-45.
    3. Guo, Haitao & Yang, Xianhui, 2008. "Automatic creation of Markov models for reliability assessment of safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 829-837.
    4. Torres-Echeverría, A.C. & Martorell, S. & Thompson, H.A., 2009. "Modelling and optimization of proof testing policies for safety instrumented systems," Reliability Engineering and System Safety, Elsevier, vol. 94(4), pages 838-854.
    5. Jin, Hui & Rausand, Marvin, 2014. "Reliability of safety-instrumented systems subject to partial testing and common-cause failures," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 146-151.
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    Cited by:

    1. Mugnaini, Marco & Addabbo, Tommaso & Fort, Ada & Elmi, Alessandro & Landi, Elia & Vignoli, Valerio, 2020. "Magnetic brakes material characterization under accelerated testing conditions," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Son, Kwang Seop & Kim, Dong Hoon & Kim, Chang Hwoi & Kang, Hyun Gook, 2016. "Study on the systematic approach of Markov modeling for dependability analysis of complex fault-tolerant features with voting logics," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 44-57.
    3. Kretzschmar, U. & Gomez-Cornejo, J. & Astarloa, A. & Bidarte, U. & Ser, J. Del, 2016. "Synchronization of faulty processors in coarse-grained TMR protected partially reconfigurable FPGA designs," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 1-9.

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