@article{ref1,
title="Fault diagnosis of subsea collet connector based on dynamic Bayesian network",
journal="China safety science journal (CSSJ)",
year="2020",
author="Chen, Z. and Liu, G. and Wang, Y. and Zhu, C. and Shan, J. and Zhai, X.",
volume="30",
number="5",
pages="81-87",
abstract="In order to diagnose fault and predict failure for mechanical structure of vertical collet subsea connectors, a fault diagnosis method based on three-layer dynamic Bayesian network is proposed. Targeting at common parts (collet, sealing ring and driving ring) and failure modes of connectors, Dynamic Bayesian network was used to simulate degradation of these materials over time, and failure data of components were obtained through expert scoring, so as to determine prior probability of fault layer state and conditional probability of symptom layer state after occurrence of fault as well as solve dynamic Bayesian network by using GeNle software. The results show that based on observed symptom layer state, change trend of posterior failure probability of three key components can be obtained, and faulty component can be found. © 2020 China Safety Science Journal. All rights reserved.<p /><p>Language: zh</p>",
language="zh",
issn="1003-3033",
doi="10.16265/j.cnki.issn1003-3033.2020.05.013",
url="http://dx.doi.org/10.16265/j.cnki.issn1003-3033.2020.05.013"
}