Induction of structure function of multi-state system based on uncertain data

2017 ◽  
Author(s):  
E Zaitseva ◽  
V Levashenko ◽  
M Kvassay
Keyword(s):  
Structure Function ◽  
Uncertain Data ◽  
State System

Reliability analysis of multi-state system with application of multiple-valued logic

2017 ◽  
Vol 34 (6) ◽  
pp. 862-878 ◽  
Author(s):  
Elena Zaitseva ◽  
Vitaly Levashenko
Keyword(s):  
Boolean Algebra ◽  
Structure Function ◽  
Reliability Analysis ◽  
Differential Calculus ◽  
Natural Extension ◽  
State System ◽  
System Component ◽  
Critical System ◽  
Content Type ◽  
Multiple Valued Logic

Purpose The purpose of this paper is to develop a new mathematical method for the reliability analysis and evaluation of multi-state system (MSS) reliability that agrees with specifics of such system. It is possible based on the application of multiple-valued logic (MVL) that is a natural extension of Boolean algebra used in reliability analysis. Design/methodology/approach Similar to Boolean algebra, MVL is used for the constriction of the structure function of the investigated system. The interpretation of the structure function of the MSS in terms of MVL allows using mathematical methods and approaches of this logic for the analysis of the structure function. Findings The logical differential calculus is one of mathematical approaches in MVL. The authors develop new method for MSS reliability analysis based on logical differential calculus, in particular direct partial logical derivatives, for the investigation of critical system states (CSSs). The proposed method allows providing the qualitative and quantitative analyses of MSS: the CSS can be defined for all possible changes of any system component or group of components, and probabilities of this state can also be calculated. Originality/value The proposed method permits representing the MSS in the form of a structure function that is interpreted as MVL function and provides the system analyses without special transformation into Boolean interpretation and with acceptable computational complexity.

Structure function in analysis of multi-state system availability

2018 ◽  
pp. 897-905
Author(s):  
M. Kvassay ◽  
V. Levashenko ◽  
J. Rabcan ◽  
P. Rusnak ◽  
E. Zaitseva
Keyword(s):  
Structure Function ◽  
State System ◽  
System Availability

scholarly journals Application of the Structure Function in the Evaluation of the Human Factor in Healthcare

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Elena Zaitseva ◽  
Vitaly Levashenko ◽  
Jan Rabcan ◽  
Emil Krsak
Keyword(s):  
Structure Function ◽  
Medical Error ◽  
System Performance ◽  
Human Factor ◽  
Uncertain Data ◽  
Human Reliability Analysis ◽  
Performance Levels ◽  
New Device ◽  
Incomplete And Uncertain Data ◽  
One Year

A structure function is one of the possible mathematical models of systems in reliability engineering. A structure function maps sets of component states into system performance levels. Methods of system reliability evaluation based on structure function representation are well established. A structure function can be formed based on completely specified data about system behavior. Such data for most real-world systems are incomplete and uncertain. The typical example is analysis and evaluation of the human factor. Therefore, the structure function is not used in human reliability analysis (HRA) typically. In this paper, a method for structure function construction is proposed based on incomplete and uncertain data in HRA. The proposed method application is considered for healthcare to evaluate medical error. This method is developed using a fuzzy decision tree (FDT), which allows all possible component states to be classified into classes of system performance levels. The structure function is constructed based on the decision table, which is formed according to the FDT. A case study for this method is considered by evaluating the human factor in healthcare: complications in the familiarization and exploitation of a new device in a hospital department are analyzed and evaluated. This evaluation shows the decreasing of medical errors in diagnosis after one year of device exploitation and a slight decrease in quality of diagnosis after two months of device exploitation. Numerical values of probabilities of medical error are calculated based on the proposed approach.

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