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)
on Marine Navigation
and Safety of Sea Transportation
Volume 1
Number 2
June 2007
199
On Ship Systems Multi-state Safety Analysis
P. Dziula, M. Jurdzinski, K. Kolowrocki & J. Soszynska
Gdynia Maritime University, Gdynia, Poland
ABSTRACT A multi-state approach to defining basic notions of the system safety analysis is proposed. A
system safety function and a system risk function are defined. A basic safety structure of a multi-state series
system of components with degrading safety states is defined. For this system the multi-state safety function is
determined. The proposed approach is applied to the evaluation of a safety function, a risk function and other
safety characteristics of a ship system composed of a number of subsystems having an essential influence on
the ship safety. Further, a semi-markov process for the considered system operation modelling is applied. The
paper also offers an approach to the solution of a practically important problem of linking the multi-state
system safety model and its operation process model. Finally, the proposed approach is applied to the
preliminary evaluation of safety characteristics of a ship system in varying operation conditions.
1 INTRODUCTION
Taking into account the importance of the safety and
operating process effectiveness of technical systems
it seems reasonable to expand the two-state approach
to multi-state approach in their safety analysis
(Dziula, Jurdzinski, Kolowrocki & Soszynska 2007).
The assumption that the systems are composed of
multi-state components with safety states degrading
in time gives the possibility for more precise analysis
and diagnosis of their safety and operational
processes’ effectiveness. This assumption allows us
to distinguish a system safety critical state to exceed
which is either dangerous for the environment or
does not assure the necessary level of its operational
process effectiveness. Then, an important system
safety characteristic is the time to the moment of
exceeding the system safety critical state and its
distribution, which is called the system risk function.
This distribution is strictly related to the system
multi-state safety function that is a basic
characteristic of the multi-state system. Determining
the multi-state safety function and the risk function
of systems on the base of their components’ safety
functions is then the main research problem.
Modelling of complicated systems operations’
processes is difficult mainly because of large number
of operations states and impossibility of precise
describing of changes between these states. One of
the useful approaches in modelling of these
complicated processes is applying the semi-markov
model (Grabski 2002). Modelling of multi-state
systems’ safety and linking it with semi-markov
model of these systems’ operation processes is the
main and practically important research problem of
this paper. The paper is devoted to this research
problem with reference to basic safety structures of
technical systems (Soszynska 2005, 2006) and
particularly to safety analysis of a ship series system
(Jurdzinski, Kolowrocki & Dziula 2006) in variable
operation conditions. This new approach to system
safety investigation is based on the multi-state
system reliability analysis considered for instance in
(Aven 1985, Hudson & Kapur 2985, Kolowrocki
2004, Lisnianski & Levitin 2003, Meng 1993, Xue
& Yang 1995) and on semi-markov processes
modelling discussed for instance in (Grabski 2002).