Fault diagnosis of subsea collet connector based on dynamic Bayesian network

doi:10.16265/j.cnki.issn1003-3033.2020.05.013

Abstract

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.

Key words: dynamic Bayesian network, subsea collet connector, fault diagnosis, expert scoring, GeNle

CLC Number:

X924.2

CHEN Zhihuang, LIU Guoheng, WANG Yingying, ZHU Chunli, SHAN Jian, ZHAI Xiaodong. Fault diagnosis of subsea collet connector based on dynamic Bayesian network[J]. China Safety Science Journal, 2020, 30(5): 81-87.

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