Izvestia Vysshikh Uchebnykh Zawedeniy. Yadernaya Energetika

The peer-reviewed scientific and technology journal. ISSN: 0204-3327

Development of estimation of parameters of risk

6/21/2017 2017 - #02 Global safety, reliability and diagnostics of nuclear power installations

Eliseyeva M.A. Malovik K.H.

DOI: https://doi.org/10.26583/npe.2017.2.03

UDC: 62.192:682.039

The production safety at projection and operation of the hazardous production facilities (HPF) is provided, in many respects, due to identification, the analysis and prediction of risk of accidents (refusals) which are carried out by means of, whenever possible, more the overall quantitative assessment of risk, when determining technical condition of HPF [1] that is carried out by Federal Service for Environmental, Technological and Nuclear Supervision of Russia (Rostekhnadzor). Among HPF with multifactorial risks at a design stage the special place belongs to objects of a nuclear power engineering, technical means for development of the shelf, to platforms for oil and gas production, and to also crucial infrastructure facilities as assets which are absolutely necessary for normal functioning of society and economy of our state in the conditions of unexpected influence of factors of catastrophic risk [2 – 4].

Problematic issues of estimation and prediction of indexes of danger of risks of adverse and emergency situations, accidents and refusals are considered in [2, 3, 5 – 8] where safety of HPF on the stipulated interval of time is defined by two major factors: probability of emergence of an adverse event (situation) and damage from this event, by means of various methods of determination of risks, including the modern achievements of asymptotic probability theory of extreme values.

For solution of problem tasks of estimation of risk questions of a parameter estimation of risk at various options of interpretation of the graphic state space of HPF are considered in this work. Features of estimation of sensitivity of risk and degree of risk and evolution of approaches for estimation of risk at projection and operation of HPF are shown. Techniques of the analysis of Big Data for management of risk are offered.

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risk degree sensitivity of risk residual risk index of indeterminacy management of risk larger data