Probabilistic methods of estimating the mean residual time before the next parameters abnormalities for monitored critical systems

Artemyev, Vladimir; Kostogryzov, Andrey; Rudenko, Jury; Kurpatov, O.; Nistratov, George; Nistratov, Andrey

doi:10.1109/icsrs.2017.8272850

Cited by 10 publications

(18 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, scientifically proven prognosis of a residual time resources is necessary for acceptance of preventive measures on timely elimination of the abnormality reasons. The above-stated characterizes an actuality of this and similar researches for different industrial areas [11][12][13][14][15][16][17].…”

Section: Estimating the Mean Residual Time Before The Next Parameter mentioning

confidence: 97%

“…The purposed approach to improve existing risk control concept includes (see Figure 8) [11][12][13][14][15][16][17]:…”

Section: The Improvement Of Existing Risk Control Conceptmentioning

confidence: 99%

“…For "System + RMS" the RMS operation quality during the time of prediction Т given is evaluated by probability Р RMS (Т given ). And, the risk of critical deviation for safety during the time of prediction Т given , designated as R RMS (Т given ), can be evaluated by the earlier methods [2][3][5][6][7][8][9][10][11][12][13][14][15][16][17]. For the usual "System without RMS," the same measures Р UC (Т given ) and R UC (Т given ) can be used with specified value of input for probabilistic modeling.…”

Section: Probabilistic Analysis Of the Remote Monitoring System Possimentioning

confidence: 99%

“…The method allows to estimate residual time before the next parameter abnormality (i.e., time before the first next coming into "red" range) [14].…”

Section: Estimating the Mean Residual Time Before The Next Parameter mentioning

confidence: 99%

“…Developing existing researches [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], this manuscript includes correct probabilistic interpretation of risk prediction effectively using "smart" systems, some original basic probabilistic models for risk prediction, the improvement of existing risk control concept, and approaches for solving some problems of industrial safety for coal branch.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Probabilistic Methods and Technologies of Risk Prediction and Rationale of Preventive Measures by Using “Smart Systems”: Applications to Coal Branch for Increasing Industrial Safety of Enterprises

Artemyev¹,

Rudenko²,

Nistratov³

2018

Probabilistic Modeling in System Engineering

Self Cite

View full text Add to dashboard Cite

Abilities of "smart systems" for processing information, adaptation to conditions of uncertainty, and performance of scientifically proven preventive actions in real time are analyzed. Basic probabilistic models and technologies for the analysis of complex systems, using "smart systems," ways of generation of probabilistic models for prognostic researches of the new systems projected, modernized, or transformed, are proposed. The proposed methods are described to predict risks to lose integrity for complex structures on the given prognostic time and rationale of preventive measures considering admissible risk, estimate "smart system" operation quality, and predict in real time the mean residual time before the next parameter abnormalities. The methods and technologies are implemented on the level of the remote monitoring systems. The application is illustrated on the examples of the jointstock company "Siberian Coal Energy Company."

show abstract

Section: Estimating the Mean Residual Time Before The Next Parameter mentioning

confidence: 97%

“…The purposed approach to improve existing risk control concept includes (see Figure 8) [11][12][13][14][15][16][17]:…”

Section: The Improvement Of Existing Risk Control Conceptmentioning

confidence: 99%

Section: Probabilistic Analysis Of the Remote Monitoring System Possimentioning

confidence: 99%

“…The method allows to estimate residual time before the next parameter abnormality (i.e., time before the first next coming into "red" range) [14].…”

Section: Estimating the Mean Residual Time Before The Next Parameter mentioning

confidence: 99%