Paper Info
Title
Reliability Evaluation of Multi-State System Based on Incompletely Specified Data and Structure Function
Abstract
The important step in reliability evaluation of complex systems is development of mathematical representation for analysis. All possible representations can be divided into two types: Binary-State Systems (BSSs) and Multi-State Systems (MSSs). A BSS is based on the assumption that the system and all of its components can be in one of only two possible states - perfectly functioning or completely failed. However, BSS is not very practical for systems that operate at several performance levels, such as distribution networks or complex socio-technical systems. The other mathematical representation for the analysis of such systems is more suitable. It is MSS that permits to define more than two states in system or component operation - from perfectly functioning through partially damaged to completely failed. The use of MSS-based mathematical representation of investigated system results in the development of special methods for reliability evaluation of such a system. The structure function based methods form one of groups of MSS evaluation methods. The structure function is mathematical representation of MSS (and BSS too) which maps components states to system performance levels. Typically, this representation is used for completely specified data. In this paper, a new method for MSS reliability analysis is considered for incompletely specified data. The novelty of this method is application of Data Mining methods for MSS structure function construction based on incompletely specified data.
Year
DOI
Venue
2019
10.1109/IDAACS.2019.8924454
2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
Keywords
Field
DocType
Availability,Multi-State System,Multiple-Valued Logic,Structure function
Complex system,State system,Computer science,Distribution networks,Theoretical computer science,Artificial intelligence,Representation (mathematics),Novelty,Structure function,Machine learning
Conference
Volume
ISBN
Citations 
2
978-1-7281-4070-4
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Elena N. Zaitseva15314.38
Vitaly G. Levashenko23912.90
Jan Rabcan301.01
Miroslav Kvassay498.85
Patrik Rusnak500.68