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A dynamic Bayesian network based reliability assessment method for short-term multi-round situation awareness considering round dependencies

Author

Listed:
  • You, Qidong
  • Guo, Jianbin
  • Zeng, Shengkui
  • Che, Haiyang

Abstract

During abnormal situations, the perception, comprehension, and projection of elements related to abnormality, i.e., situation awareness (SA), is one of the critical factors to guarantee system safety, and dynamically achieved in short-term multi-round interactions. The short-term multi-round SA (STMR-SA) experiences round dependencies due to anchoring effect (AE) and confirmation bias (CB). For SA in current round, AE biases judgement of system states towards previous cognition, and CB leads to neglect of information disproving previous opinion. Therefore, AE and CB mutually promote and impede the correction of STMR-SA errors. The effects of AE and CB on STMR-SA have been verified in qualitative research but disregarded in quantitative STMR-SA reliability assessments. This paper aims to propose a novel STMR-SA reliability assessment method considering AE-caused and CB-caused round dependencies. First, a round dependency model (RDM) is developed to quantify above-mentioned round dependencies. Subsequently, STMR-SA evolution is modeled with dynamic Bayesian network, where round dependencies are represented by connections between adjacent time slices and quantified by RDM. Case study on Boeing 737-8 (MAX) accident demonstrates the effectiveness of this method. Results indicates that improvement measures including adding angle of attack (AOA) disagree warning and training enhancement, could improve the STMR-SA reliability and system safety.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007524
    DOI: 10.1016/j.ress.2023.109838
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