Analysis of connection fault and service maintenance strategy for subsea horizontal clamp connector

• Published in: Ocean engineering Vol. 307; p. 118257
• Main Authors: Liu, Weifeng, Yun, Feihong, Ju, Ming, Yao, Shaoming, Chen, Xi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2024
• Subjects: Connection fault; Dynamic bayesian network; FMECA-BN; Maintenance strategy; Subsea horizontal clamp connector; Maintenance strategy; Dynamic bayesian network; Subsea horizontal clamp connector; FMECA-BN; Connection fault
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2024.118257

The deep-sea environment restricts the collection of reliable data for subsea connectors, leading to a lack of available failure data to develop connection fault detection processes and maintenance strategies. Therefore, this paper proposes an FMECA-BN model. The model developed an algorithm for mapping Risk Priority Number (RPN) to Bayesian network (BN) parameters and enhanced the details for mapping BN structures to match the diversity of FMEA. This enables the establishment of a BN based on a given Failure Mode, Effects, and Criticality Analysis (FMECA). The model was applied to the development of the Subsea Horizontal Clamp Connector (SHCC) to quantify and evaluate failure modes during the connection stage, creating an effective detection process. Then, the system's reliability variations caused by component degradation were analyzed using transformed dynamic BN, offering information for maintenance strategies based on availability. The reliability analysis results of the FMECA-BN model demonstrate a statistical error of 4.5% compared to actual data samples, proving the model's efficacy in addressing issues of information asymmetry, incomplete reliability data, and uncertainty. The analysis revealed that the developed SHCC requires a maintenance cycle of 14,892 h to maintain a 0.99 reliability level, with a cumulative failure probability of 4.6% after 20 years. •This paper presents an FMECA-BN model that incorporates the functionality of mapping Risk Priority Number (RPN) to Bayesian network node parameters to address the challenge of insufficient prior data.•This paper is based on the engineering context and delves into the specific process and characteristics of failure modes occurring in the developed subsea horizontal clamp connector (SHCC). The conversion of static Bayesian networks to dynamic Bayesian networks has been employed to accomplish the reliability analysis and data update for the SHCC.•For the first time, a quantitative analysis of SHCC is conducted at both the system and component levels, effectively addressing the issues related to connection fault detection and formulating service and maintenance strategies based on availability.