TY - JOUR
T1 - A multinomial process tree for reliability assessment of machinery in autonomous ships
AU - Abaei, Mohammad Mahdi
AU - Hekkenberg, Robert
AU - BahooToroody, Ahmad
PY - 2021
Y1 - 2021
N2 - Maritime Autonomous Surface Ships have received a significant amount of attention in recent projects. They promise a reduction in marine accidents and mitigation of human errors. Most of the ongoing research effort is directed toward autonomous navigation and cybersecurity. However, the importance of a machinery plant in the engine room that can operate reliably without human attendance is hardly investigated. To prevent failures in such systems and extend the interval between required human interventions, it is essential to improve their reliability. This paper aims to present a systematic approach to evaluate the reliability of an autonomous system under the influence of uncertain disruptions and to predict failure rates of unattended machinery plants. A Multinomial Process Tree is used to model failures in the main failure-sensitive components. Hierarchical Bayesian Inference is adopted to facilitate the prediction of frequencies of disruptive events and estimate the entire system's failure rate. The outcome of this research enables design strategies to improve the reliability of autonomous ships and prevent Fatal Technical Failure during the operation. This allows assessing whether a given machinery plant is sufficiently reliable to be used on unmanned ships. A case study is considered to demonstrate the application of the presented method.
AB - Maritime Autonomous Surface Ships have received a significant amount of attention in recent projects. They promise a reduction in marine accidents and mitigation of human errors. Most of the ongoing research effort is directed toward autonomous navigation and cybersecurity. However, the importance of a machinery plant in the engine room that can operate reliably without human attendance is hardly investigated. To prevent failures in such systems and extend the interval between required human interventions, it is essential to improve their reliability. This paper aims to present a systematic approach to evaluate the reliability of an autonomous system under the influence of uncertain disruptions and to predict failure rates of unattended machinery plants. A Multinomial Process Tree is used to model failures in the main failure-sensitive components. Hierarchical Bayesian Inference is adopted to facilitate the prediction of frequencies of disruptive events and estimate the entire system's failure rate. The outcome of this research enables design strategies to improve the reliability of autonomous ships and prevent Fatal Technical Failure during the operation. This allows assessing whether a given machinery plant is sufficiently reliable to be used on unmanned ships. A case study is considered to demonstrate the application of the presented method.
KW - Autonomous shipping
KW - Bayesian inference
KW - Machinery plant
KW - Multinomial process tree
KW - Reliability engineering
UR - http://www.scopus.com/inward/record.url?scp=85100492077&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2021.107484
DO - 10.1016/j.ress.2021.107484
M3 - Article
AN - SCOPUS:85100492077
SN - 0951-8320
VL - 210
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
M1 - 107484
ER -