Helicopter MGB oil system failure analysis using influence diagrams and random failure probabilities

Rashid, Hamad; Place, C. S.; Lim, Reuben; Healey, A.; Kleine-Beek, W.; Romano, Maria

doi:10.1016/j.engfailanal.2014.12.013

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Another discipline that uses BDNs to address complex decision-making processes is engineering. For example, Rashid et al 98 used a BDN model to investigate oil system failure analysis in helicopters, focusing on random failure probabilities. In an example from IT, Christoforou et al 99 successfully used a BDN model to investigate and determine cloud adaptability for IT services.…”

Section: Created the Technique Calledmentioning

confidence: 99%

Predicting locally manageable resource failures of high availability clusters

Somasekaram

Călinescu

2022

Softw Pract Exp

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Critical services from domains as diverse as finance, manufacturing and healthcare are often delivered by complex enterprise applications (EAs). High-availability clusters (HACs) are software-managed IT infrastructures that enable these EAs to operate with minimum downtime. This paper presents a novel Bayesian decision network model to improve the failure detection capabilities of the HACs components using a comprehensive set of characteristics for the analysed component. The model then combines these characteristics to predict whether the failure of this component can be managed locally at the failed component level without propagating the failure to upper-level components and causing a complete system failure. By improving the detection capabilities and predicting locally manageable failures, the model improves the decision-making process of HACs, and has the potential to reduce the downtime and improve availability for the applications protected by HACs. The model uses the capabilities of the Bayesian decision networks, which combines Bayesian networks with the utility theory, to assign weights to different characteristics and consolidate the related variables to output the result. The model evaluation in a realistic testbed environment with three servers, an established HAC and a well-known EA shows that the model can improve the area under the Receiver Operating Characteristic (ROC) curve for prediction of locally manageable failures by up to 9.05% compared to the baseline HAC results.

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Section: Created the Technique Calledmentioning

confidence: 99%

Predicting locally manageable resource failures of high availability clusters

Somasekaram

Călinescu

2022

Softw Pract Exp

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“…e ID model has been widely applied in multiple fields, such as the medical field [45,46], military applications [47], the human-robot communication for collaborative decision-making [48], and industrial engineering [49]. By employing ID to analyze the reliability of helicopter's main gearbox system and the exact influence of each of its internal components or external factors (organizational/human), Rashid et al [50] identified and estimated the maximum "permissible" probability of failure for a given component. Meanwhile, a selected set of governing failure probability is reached.…”

Section: Influence Diagram Eory As a Powerful Investigation Techniqumentioning

confidence: 99%

Analysis of Failures and Influence Factors of Critical Infrastructures: A Case of Metro

Deng

Song

Zhou

et al. 2020

Advances in Civil Engineering

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Metro plays an indispensable role in promoting social and economic development of the big cities. However, the safety operation of the metro system is believed to be an arduous work for its operation being susceptible to various factors, especially equipment failure, which could bring about delay, interruption, and even accidents. In consideration of the seriousness of metro accident, it is becoming more and more crucial to conduct proactive, targeted, and effective risk management to reduce failures. Firstly, based on the FMECA and influence diagram theory, this paper proposes a new analytical framework to study metro equipment failure in accordance with system decomposition. Furthermore, a case study is implemented to verify the feasibility and availability of this framework in evaluating criticality of failure modes and assessing its influence factors based on the collected data from metro operation company. As indicated by the research results, wheel damage is the most serious failure mode in the bogie system, and its most paramount influence factor is the shortage of professional skills. Finally, a discussion about the research methods and results is carried out, and several recommendations are put forward to improve safety management on the basis of research findings. This study has the potential to offer suggestions in regard to metro equipment design, operation, and maintenance for raising the safety level in metro operation.

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Fault Diagnosis and Fault-Tolerant Control of Helicopters

Jiang,

Zhang,

et al. 2024

Reference Module in Materials Science and Materials Engineering

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Helicopter MGB oil system failure analysis using influence diagrams and random failure probabilities

Cited by 9 publications

References 12 publications

Predicting locally manageable resource failures of high availability clusters

Predicting locally manageable resource failures of high availability clusters

Analysis of Failures and Influence Factors of Critical Infrastructures: A Case of Metro

Fault Diagnosis and Fault-Tolerant Control of Helicopters

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