Fuzzy Fault Tree Analysis for Structural Safety

Fujino, Tomoyuki; Hadipriono, Fabian C.

doi:10.3233/ifs-1996-4403

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…However, applying BRB to the actual fault diagnosis of the flywheel system cannot solve problems such as the difficulty in constructing an expert knowledge base, the unclear logical relationship between the flywheel fault events and the unclear fault index. FFTA (fuzzy fault tree analysis) [15,16] enables the logical relationship between different events to be clearly expressed. This is because FFTA can present the cause of failure and events caused by this cause in the form of a fault tree from the perspective of the fault mechanism.…”

Section: Introductionmentioning

confidence: 99%

A Model for Flywheel Fault Diagnosis Based on Fuzzy Fault Tree Analysis and Belief Rule Base

Cheng¹,

Liu²,

He³

et al. 2022

Machines

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In the fault diagnosis of the flywheel system, the input information of the system is uncertain. This uncertainty is mainly caused by the interference of environmental factors and the limited cognitive ability of experts. The BRB (belief rule base) shows a good ability for dealing with problems of information uncertainty and small sample data. However, the initialization of the BRB relies on expert knowledge, and it is difficult to obtain the accurate knowledge of flywheel faults when constructing BRB models. Therefore, this paper proposes a new BRB model, called the FFBRB (fuzzy fault tree analysis and belief rule base), which can effectively solve the problems existing in the BRB. The FFBRB uses the Bayesian network as a bridge, uses an FFTA (fuzzy fault tree analysis) mechanism to build the BRB’s expert knowledge, uses ER (evidential reasoning) as its reasoning tool, and uses P-CMA-ES (projection covariance matrix adaptation evolutionary strategies) as its optimization model algorithm. The feasibility and superiority of the proposed method are verified by an example of a flywheel friction torque fault tree.

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Section: Introductionmentioning

confidence: 99%