IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v207y2021ics0951832020308292.html
   My bibliography  Save this article

A novel dynamic function allocation method in human-machine systems focusing on trigger mechanism and allocation strategy

Author

Listed:
  • Atashfeshan, Nooshin
  • Saidi-Mehrabad, Mohammad
  • Razavi, Hamideh

Abstract

In view of human factors, increased automation may result in deterioration of situation awareness, unbalanced workload and skill degradation as a cost of human increasingly being ‘out-of-the-loop’. The concept of dynamic function allocation (DFA) was suggested to resolve these disadvantages by changing the levels of human/machine's controls over system functions. Despite the importance of function allocation, there are limited methods explicitly working out the challenges of DFA in real-world applications. In order to improve system reliability and reduce ‘out-of-the-loop’ problems, the present study focuses on the determination of the reallocation times known as the trigger mechanism and the way of reallocating as the allocation strategy. To this end, the prediction of agent reliability is an essential step. To handle uncertainty arising from the subjectivity of analysis and lack of data, the Bayesian network is used to predict system performance. As a case study, the interaction of humans with a distributed control system used in a gas power plant is considered. By applying the proposed DFA method, human situation awareness and system reliability would be improved by approximately 30%, specifically under time pressure, and inappropriate work environment. This approach, as decision support, provides guidance for automation designers to improve an automated system.

Suggested Citation

  • Atashfeshan, Nooshin & Saidi-Mehrabad, Mohammad & Razavi, Hamideh, 2021. "A novel dynamic function allocation method in human-machine systems focusing on trigger mechanism and allocation strategy," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308292
    DOI: 10.1016/j.ress.2020.107337
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832020308292
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2020.107337?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    2. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    3. Lee, Hyun-Chul & Seong, Poong-Hyun, 2009. "A computational model for evaluating the effects of attention, memory, and mental models on situation assessment of nuclear power plant operators," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1796-1805.
    4. Hogenboom, Sandra & Rokseth, Børge & Vinnem, Jan Erik & Utne, Ingrid Bouwer, 2020. "Human reliability and the impact of control function allocation in the design of dynamic positioning systems," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    5. Røed, Willy & Mosleh, Ali & Vinnem, Jan Erik & Aven, Terje, 2009. "On the use of the hybrid causal logic method in offshore risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 445-455.
    6. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud & van Gelder, Pieter, 2020. "BN-SLIM: A Bayesian Network methodology for human reliability assessment based on Success Likelihood Index Method (SLIM)," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    7. Gregoriades, Andreas & Sutcliffe, Alistair, 2008. "Workload prediction for improved design and reliability of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 93(4), pages 530-549.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    2. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Asadayoobi, N. & Taghipour, S. & Jaber, M.Y., 2022. "Predicting human reliability based on probabilistic mission completion time using Bayesian Network," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2016. "Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 93-112.
    5. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    6. Bandeira, Michelle Carvalho Galvão Silva Pinto & Correia, Anderson Ribeiro & Martins, Marcelo Ramos, 2018. "General model analysis of aeronautical accidents involving human and organizational factors," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 137-146.
    7. Pandya, Dhruv & Podofillini, Luca & Emert, Frank & Lomax, Antony J. & Dang, Vinh N. & Sansavini, Giovanni, 2020. "Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
    8. Fam, Mei Ling & He, Xuhong & Konovessis, Dimitrios & Ong, Lin Seng, 2020. "Using Dynamic Bayesian Belief Network for analysing well decommissioning failures and long-term monitoring of decommissioned wells," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    9. Maturana, Marcos Coelho & Martins, Marcelo Ramos & Frutuoso e Melo, Paulo Fernando Ferreira, 2021. "Application of a quantitative human performance model to the operational procedure design of a fuel storage pool cooling system," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Abreu, Danilo T.M.P. & Maturana, Marcos C. & Droguett, Enrique Lopez & Martins, Marcelo R., 2022. "Human reliability analysis of conventional maritime pilotage operations supported by a prospective model," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    11. Hogenboom, Sandra & Parhizkar, Tarannom & Vinnem, Jan Erik, 2021. "Temporal decision-making factors in risk analyses of dynamic positioning operations," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    12. Baoping Cai & Yonghong Liu & Zengkai Liu & Xiaojie Tian & Yanzhen Zhang & Renjie Ji, 2013. "Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations," Risk Analysis, John Wiley & Sons, vol. 33(7), pages 1293-1311, July.
    13. Morais, Caroline & Estrada-Lugo, Hector Diego & Tolo, Silvia & Jacques, Tiago & Moura, Raphael & Beer, Michael & Patelli, Edoardo, 2022. "Robust data-driven human reliability analysis using credal networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    14. You, Qidong & Guo, Jianbin & Zeng, Shengkui & Che, Haiyang, 2024. "A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    15. Abrishami, Shokoufeh & Khakzad, Nima & Hosseini, Seyed Mahmoud, 2020. "A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    16. Zhou, Jian-Lan & Lei, Yi, 2020. "A slim integrated with empirical study and network analysis for human error assessment in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    17. Li, Jue & Li, Heng & Wang, Fan & Cheng, Andy S.K. & Yang, Xincong & Wang, Hongwei, 2021. "Proactive analysis of construction equipment operators’ hazard perception error based on cognitive modeling and a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    18. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zio, Enrico & Yuan, Chengwei & Wang, Taorui & Jiang, Jianjun, 2022. "A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    19. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2023. "A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    20. Quintanar-Gago, David A. & Nelson, Pamela F. & Díaz-Sánchez, à ngeles & Boldrick, Michael S., 2021. "Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:207:y:2021:i:c:s0951832020308292. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.