Human Reliability Analysis for Visual Inspection in Aviation Maintenance by a Bayesian Network Approach

Chen, Wei; Huang, Shupei

doi:10.3141/2449-12

Cited by 19 publications

(8 citation statements)

References 11 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BN modeling steps are summarized as follows (see Figure 5 ) [ 13 , 14 ]: Step 1: Definition and identification of influence factors. Step 2: Construction of the BN model, which includes development of the network and elicitation of the CPTs.…”

Section: Methodsmentioning

confidence: 99%

“…The BN modeling process and application in the field of aviation safety (especially about inspection and aviation maintenance) have been discussed in the authors’ previous studies [ 13 , 14 ]. Nevertheless, only two states are set for most nodes in the network due to the limitations of the noisy OR gates and the recursive noisy OR rule, which are used to generate CPTs (Conditional Probability Table).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Flight Crew Performance by a Bayesian Network Model

Chen

Huang

2018

Entropy

Self Cite

View full text Add to dashboard Cite

Flight crew performance is of great significance in keeping flights safe and sound. When evaluating the crew performance, quantitative detailed behavior information may not be available. The present paper introduces the Bayesian Network to perform flight crew performance evaluation, which permits the utilization of multidisciplinary sources of objective and subjective information, despite sparse behavioral data. In this paper, the causal factors are selected based on the analysis of 484 aviation accidents caused by human factors. Then, a network termed Flight Crew Performance Model is constructed. The Delphi technique helps to gather subjective data as a supplement to objective data from accident reports. The conditional probabilities are elicited by the leaky noisy MAX model. Two ways of inference for the BN-probability prediction and probabilistic diagnosis are used and some interesting conclusions are drawn, which could provide data support to make interventions for human error management in aviation safety.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Flight Crew Performance by a Bayesian Network Model

Chen

Huang

2018

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Such a trend shows that although the probability-based risk theories truly have initial effects on risk reduction, their component failure-based vision still has embedded roots in static and linear safety philosophy, and can hardly address accident causality involving interactive complexity. Facing this, other researchers in this field have made efforts to develop extensive techniques to investigate human reliability factors such as Petri nets, the dynamic Bayesian network, and statecharts [8][9][10]. However, these methods can not address the dynamic processes of risk transferring, especially in human and organizational levels.…”

Section: Of 20mentioning

confidence: 99%

Ensuring the Safety Sustainability of Large UAS: Learning from the Maintenance Risk Dynamics of USAF MQ-1 Predator Fleet in Last Two Decades

et al. 2019

View full text Add to dashboard Cite

The mishap statistics of large military unmanned aerial systems (UAS) reveal that human errors and organizational flaws pose great threats to their operation safety, especially considering the future application of derived civilian types. Moreover, maintenance accidents due to human factors have reached a significant level, but have received little attention in the existing research. To ensure the safety and sustainability of large UAS, we propose a system dynamics approach to model the maintenance risk mechanisms involving organizational, human, and technical factors, which made a breakthrough in the traditional event-chain static analysis method. Using the United States Air Force (USAF) MQ-1 Predator fleet case, the derived time-domain simulation represented the risk evolution process of the past two decades and verified the rationality of the proposed model. It was identified that the effects of maintainer human factors on the accident rate exceeded those of the technical systems in a long-term view, even though the technical reliability improvements had obvious initial effects on risk reduction. The characteristics of maintainer errors should be considered in system and maintenance procedure design to prevent them in a proactive way. It is also shown that the approach-derived SD model can be developed into a semi-quantitative decision-making support tool for improving the safety of large UAS in a risk-based view of airworthiness.

show abstract

“…• In the area of maintenance, BNs are also applied for improving Human reliability analysis (HRA) in visual inspection [25].…”

Section: Bayesian Network For Decision-making In Aviationmentioning

confidence: 99%

Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

Valdés¹,

Comendador²,

Sanz³

et al. 2019

Bayesian Networks - Advances and Novel Applications

View full text Add to dashboard Cite

Most decisions in aviation regarding systems and operation are currently taken under uncertainty, relaying in limited measurable information, and with little assistance of formal methods and tools to help decision makers to cope with all those uncertainties. This chapter illustrates how Bayesian analysis can constitute a systematic approach for dealing with uncertainties in aviation and air transport. The chapter addresses the three main ways in which Bayesian networks are currently employed for scientific or regulatory decisionmaking purposes in the aviation industry, depending on the extent to which decision makers rely totally or partially on formal methods. These three alternatives are illustrated with three aviation case studies that reflect research work carried out by the authors.

show abstract

Human Reliability Analysis for Visual Inspection in Aviation Maintenance by a Bayesian Network Approach

Cited by 19 publications

References 11 publications

Evaluating Flight Crew Performance by a Bayesian Network Model

Evaluating Flight Crew Performance by a Bayesian Network Model

Ensuring the Safety Sustainability of Large UAS: Learning from the Maintenance Risk Dynamics of USAF MQ-1 Predator Fleet in Last Two Decades

Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

Contact Info

Product

Resources

About