Survey on Reliability Analysis of Dynamic Positioning Systems
SHIPS AND OFFSHORE STRUCTURES(2024)
Hangzhou City Univ | Zhejiang Univ
Abstract
Technological advancements and the expanding scope of offshore dynamic positioning (DP) applications introduce new challenges in terms of safety and risk control. This paper provides an overview of a proposed methodology for reliability and risk analysis in the context of DP operations. Firstly, two typical DP operation contexts are discussed. Subsequently, a comprehensive survey is conducted to assess the reliability analysis methodologies applicable to DP systems, encompassing techniques such as Failure Mode and Effects Analysis (FMEA), fault tree analysis, Markov models, and Bayesian networks, which are employed to evaluate the technical and human reliability of DP Class 2 and Class 3 systems. Finally, the paper concludes with a discussion of future research directions and challenges pertaining to reliability analysis and risk management in DP operations.
MoreTranslated text
Key words
Reliability analysis,dynamic positioning,FMEA,Markov model,fault tree,human reliability
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined