Work

Learning of Probability Estimates for System and Network Reliability Analysis by Means of Matrix Learning Vector Quantization

Year: 2025

Type: conference-paper

Abstract: We present a new approach for the assessment of the reliability of coherent systems by using a prototype-based classification method.More specifically, reliability levels for consecutive k-out-of-n systems, which serve as a model for a particular type of networks, are classified using Generalized Matrix Learning Vector Quantization, which provides useful information about the impact of the input probabilities on the classified reliability levels.Our approach is not limited to reliability analysis, but is generally applicable for estimating the probability of the union of any finite family of events, based on their individual and pairwise probabilities.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-67

Authors: Mandy Lange-Geisler, Klaus Dohmen, Thomas Villmann

Institutions: Hochschule Mittweida, TU Bergakademie Freiberg

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409455405

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