Recognition of Incipient Defects in the Units of Ship Machinery by Vibrodiagnostics Based on Optimum Decision Rules

Davydov, V. S.

doi:10.1134/s1061830919030045

Cited by 6 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Поэтому целесообразно определять плотность вероятности 12 ( , , ..., ) K f r r r без предварительного определения факта зависимости или независимости случайных величин. Это может быть сделано с помощью метода восстановления плотности вероятности Парзена-Розенблатта [7][8][9][10]. При использовании этого метода в данном исследовании плотность вероятности 12 ( , , ..., ) K f r r r определялась согласно [11] по формуле…”

Section: рис 1 структурная схема методики оценивания адекватности имunclassified

Methodology for Assessing the Adequacy of Statistical Simulation Models

Vivchar,

Ptushkin,

Sokolov

2023

MMPh

View full text Add to dashboard Cite

Statistical simulation models of complex technical systems characterized by several indicators of operational efficiency were studied in this paper. The efficiency of obtaining knowledge on the examined systems depends on the quality of the models used. One of the basic properties describing the quality of a model is its adequacy – the complex property characterizing the degree of conformity of the values of the output parameters of the model with the object with the required accuracy and reliability. Current approaches to evaluating the adequacy of models are based on various subjective convolutions of confidence factors from research results to a generalized indicator the essence of which, as a rule, is not interpreted. The presented method of assessing the adequacy of statistical simulation models of complex technical systems with several performance indicators differs from existing methods by using a generalized indicator of adequacy, which is the probability of achieving the required confidence of all the accuracy requirements to determine each of the considered performance indicators. This indicator is a natural, unambiguously interpreted (the probability of satisfying the requirements for model adequacy) objective and generalized indicator of adequacy of the simulation model. For preliminary calculations we use the Parzen–Rosenblatt method and obtain the probability density function of distances between real and model indicators of effectiveness of the examined system. The required result is then obtained by the suggested algorithm of multiple integration of the density function using the Monte-Carlo method. Recommendations on the realization of the computational procedures foreseen by the method are given. The application of the method is illustrated by a description of a computational experiment.

show abstract

Section: рис 1 структурная схема методики оценивания адекватности имunclassified