The logarithmic basis to measure the amount of information related to the assessment of reliability of elements of the technical system

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Dulesov, A.S.^1 ; Karandeev, D.J.^1 ; Eremeeva, O.S.^1 ; Khrustalev, V.I.^1 ; Dulesova, N.V.^2

Katanov Khakass State University, Lenina ave. 92, Abakan

655017, Russia^1

Khakas Technical Institute, Siberian Federal University, Komarova ave. 15, Abakan

655017, Russia^2

关键词: Amount of information;    Exponential distributions;    Information entropy;    Mathematical expressions;    Natural logarithms;    Probability of failure;    Reliability Evaluation;    Reliability indicators;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/5/052003/pdf
DOI  :  10.1088/1757-899X/537/5/052003

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

The possibilities of application of logarithmic measure in the problem of reliability evaluation of elements of a technical system are considered. The article presents a brief analysis of the processes of additive and multiplicative growth of system indicators, among which the probability of failure-free operation and failure rate are considered. These indicators were considered in relation to the exponential distribution law of random variables associated with multiplicative growth. The growth of reliability indicators is expressed through the natural logarithm, which allows to determine the amount of information belonging to the element of the system. Considering the additive growth and multiplicative growth in the aggregate, the mathematical expressions of the determination of information entropy in the case of operation of the technical system are presented. The obtained quantitative entropy values are the basis for assessing the state and level of reliability of the system. An example of the calculation is given, confirming the importance of the participation amount of entropy in the problem of estimating the reliability of systems.