원문정보
초록
영어
The effectiveness of a submarine casing cutting robot is mainly influenced not only by its operational but also by its reliability and safety. In this paper, fault diagnosis research of this cutting robot is evaluated using the Bayesian network. A methodology of transforming the fault tree model into Bayesian network model is used. The fault tree model is established simply and conveniently. Bayesian network can address interesting questions allowing both forward and backward analysis. Combining the merits of two methods, the causes of failures, the occurrence probabilities and the importance of various components are analyzed based on the Netica software. The results show that the robot has high reliability and should be paid attentions to the research of feeding mechanism and the discharge gap detection circuits.
목차
1. Introduction
2. Submarine Casing Cutting Robot
3. Fault Diagnosis System Based on Bayesian Network
4. Model Methodology
4.1. The Establishment of Fault Tree Model
4.2. Translate the Fault Tree Model into Bayesian Network
4.3. Validation of the Model
5. Fault Diagnosis of Submarine Casing Cutting Robot
5.1. Establishing the Bayesian Network Model for Submarine Casing Cutting Robot
5.2. Validation of the Model
5.3. Fault Diagnosis of the Submarine Casing Cutting Robot
6. Conclusions
Acknowledgements
References