Statistical estimation for a failure model with damage accumulation in a case of small samples

Andronov, Alexander; Fioshina, Helen; Fioshin, Maxim

doi:10.1016/j.jspi.2008.05.026

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…The model with accumulation of damages was considered in (Afanasyeva 2002) and (Andornov, Afanasyeva, and Fioshin 2006). It is based on the failure model and its modifications, presented in (Andronov and Gertsbakh 1972;Andronov 1994;Gertsbakh 2000).…”

Section: The Case Of Partially Known Distributionsmentioning

confidence: 99%

“…Resampling approach was investigated from 1995 under prof. A. Andronov supervision. During this investigation, simple and hierarchical resampling (Andronov, Merkuryev, and Loginova 1995) and their implementations in reliability theory, queuing theory (Andronov and Fioshin 1999a;Afanasyeva 2002), stochastic processes (Andronov 2000;Afanasyeva 2005a;Andornov, Afanasyeva, and Fioshin 2006), optimization tasks (Andronov and Merkuryev 2000) and construction of confidence intervals (Andronov 2002;Andronov and Fioshin 2004) were considered. The present paper is devoted to the description of the main results connected with the reliability problems.…”

Section: Introductionmentioning

confidence: 99%

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Resampling Approach to the Estimation of Reliability Systems

Fioshin,

Fioshina

2013

Preprint

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The article is devoted to the resampling approach application to the reliability problems. This approach to reliability problems was first proposed by Ivnitsky (1967). Resampling is intensive statistical computer method, which is non-parametrical, that uses initial samples data in different combinations to simulate the process many times and get finally the estimator of the characteristics of interest. At the present paper simple resampling, hierarchical resampling, the case of one sample for several variables, the case of partially known distributions, analysis of degradation flow, analysis of degradation-renewal process, construction of confidence intervals are described. All those resampling application cases can be applied successfully to solve the reliability problems as an alternative to classical methods. 1

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Section: The Case Of Partially Known Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resampling Approach to the Estimation of Reliability Systems

Fioshin,

Fioshina

2013

Preprint

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“…In order to avoid this difficulty, we use the resampling of current edge travel times ( [2]). We repeat r times the following resampling procedure.…”

Section: Strategic Planningmentioning

confidence: 99%

“…For strategic planning, we modify the algorithm for stochastic shortest path calculation R-SSPPR ( [3], [14]) by using Bayes posterior probabilities for historical information as well as performing resampling ( [2]) to forecast unknown travel times. For tactical planning we use distributed multi-agent reinforcement learning (DEC-MARL, [8], [11]) to learn the optimal cooperative actions of agents.…”

Section: Introductionmentioning

confidence: 99%

Reconciling Strategic and Tactical Decision Making in Agent-Oriented Simulation of Vehicles in Urban Traffic

Fiošins¹,

Fiošina²,

Müller³

et al. 2011

Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques

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We consider an integrated decision making process of autonomous vehicles in agent-oriented simulation of urban traffic systems. In our approach, the planning process for a vehicle agent is separated into two stages: strategic planning and tactical planning. During the strategic planning stage the vehicle agents constructs the optimal route from source to destination; during the tactical planning stage the operative decisions such as speed regulation and lane change are considered. For strategic planning we modify the stochastic shortest path algorithm with imperfect knowledge about network conditions. For tactical planning we apply distributed multiagent reinforcement learning with other vehicles at the same edge. We present planning algorithms for both stages and demonstrate interconnections between them; an example illustrates how the proposed approach may reduce travel time of vehicle agents in urban traffic.

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Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network

Fiošina

Fiošins

2013

Distributed Computing and Artificial Intelligence

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Statistical estimation for a failure model with damage accumulation in a case of small samples

Cited by 7 publications

References 2 publications

Resampling Approach to the Estimation of Reliability Systems

Resampling Approach to the Estimation of Reliability Systems

Reconciling Strategic and Tactical Decision Making in Agent-Oriented Simulation of Vehicles in Urban Traffic

Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network

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