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Elaboration of stochastic models to comprehensive evaluation of occupational risks in complex dynamic systems

Bochkovskyi A. P.

Journal of Achievements in Materials and Manufacturing Engineering

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2021

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Vol. 104, nr 1

31--41

EN

Purpose: Elaborate stochastic models to comprehensive evaluation of occupational risks in “man - machine - environment” systems taking into account the random and dynamic nature of the impact on the employee of negative factors over time. Design/methodology/approach: Within study, the methods of probability theory and the theory of Markov processes - to find the limit distribution of the random process of dynamic impact on the employee of negative factors over time and obtain main rates against which the level of occupational risks within the "man - machine - environment" systems can be comprehensively evaluated; Erlang phases method, Laplace transform, difference equations theory, method of mathematical induction - to elaborate a method of analytical solution of the appropriate limit task for a system of differential equations in partial derivatives and appropriate limit conditions were used. Findings: A system of differential equations in partial derivatives and relevant limit conditions is derived, which allowed to identify the following main rates for comprehensive evaluation of occupational risks in systems "man - machine - environment": probability of excess the limit of the employee's accumulation of negative impact of the harmful production factor; probability of the employee’s injury of varying severity in a random time. An method to the solution the limit task for a system of differential equations, which allows to provide a lower bounds of the probability of a certain occupational danger occurrence was elaborated. Research limitations/implications: The elaborated approach to injury risk evaluation is designed to predict cases of non-severe injuries. At the same time, this approach allows to consider more severe cases too, but in this case the task will be more difficult. Practical implications: The use of the elaborated models allows to apply a systematic approach to the evaluation of occupational risks in enterprises and to increase the objectivity of the evaluation results by taking into account the real characteristics of the impact of negative factors on the employee over time. Originality/value: For the first time, a special subclass of Markov processes - Markov drift processes was proposed and substantiated for use to comprehensive evaluation of occupational risks in “man - machine - environment” systems.

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Nonlinear filtering for Markov systems with delayed observations

Calzolari A., Florchinger P., Nappo G.

International Journal of Applied Mathematics and Computer Science

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2009

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Vol. 19, no 1

49-57

EN

This paper deals with nonlinear filtering problems with delays, i.e., we consider a system (X,Y ), which can be represented by means of a system [...], in the sense that [...], where a(t) is a delayed time transformation. We start with X being a Markov process, and then study Markovian systems, not necessarily diffusive, with correlated noises. The interest is focused on the existence of explicit representations of the corresponding filters as functionals depending on the observed trajectory. Various assumptions on the function a(t) are considered.

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Equations for the Response Probability Density of Dynamic Systems under Multi-Component Non-Poisson Impulse Process Excitations

Iwankiewicz R.

Machine Dynamics Problems

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2008

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Vol. 32, No. 4

24-36

EN

The excitation considered in the present paper consists of n statistically independent random trains of impulses, each of whom is driven by a non-Poisson, renewal process with inter-arrival times being the sum of two independent negative-exponential distributed random variables with parameters vv, Vs, µs (S = 1, 2, ..., n). Each of the original impulse processes is recast into a Poisson driven impulse process with the aid of an auxiliary, purely jump stochastic variable. Each auxiliary variable is governed by the stochastic differential equation driven by two independent Poisson processes, with parameters Vs, µs, thus it is tantamount to two Markov states. The Markov chain for the whole problem is constructed by considering the coincidences of the states of the individual jump processes. The necessary so-called jump probability intensity functions are determined for all state variables and all possible jumps. The equations governing the joint probability density-distribution function of the response and of the Markov states of the auxiliary variables are derived from the general integro-differential forward Chapman-Kolmogorov equation. The resulting equations form a set of integro-partial differential equations.