Data-Driven System Reliability and Failure Behavior Modeling Using FMECA

Abstract

Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice (as shown in 8.1.9.B, above) and, when published, a full citation to the original IEEE publication, including a Digital Object Identifier (DOI). Authors shall not post the final, published versions of their articles.

Security Events and Vulnerability Data for Cybersecurity Risk Estimation

“…Each dot represents a service type, and its position on the graph indicates the relation between network alarms to which it is exposed (vertical axis, logarithmic) and the probability that an attacker of network power k = 8 successfully breaches the system. The farther toward the top-right corner a service type is, the higher the associated likelihood according to Equation (7). Services on the bottom left of the plot have low exposure and low probability of first breach.…”

“…Partially addressing these issues, recent research advancements propose new data-extraction algorithms and models for big data. For example, Khorshidi et al (7) proposes a technique for the aggregation of qualitative data features with the aim of fostering the risk management activities of complex systems whose data sources may be incomplete or not sufficient for the purpose of the analysis. Similarly, but going in the opposite direction, Susto et al (8) propose a method for the aggregation of multiple data sources to build models of the data with interpretable regressors.…”

Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications

“…In the paper "Datadriven system reliability and failure behavior modelling using FMECA," Khorshidi et al [16] examined a data-driven system to evaluate reliability of industrial systems using FMEA. An algorithm is proposed using soft computing techniques for risk management of complex systems based on qualitative data.…”

Application of Bayesian Networks in Reliability Evaluation

“…Bayesian networks (BNs) are important probabilistic directed acyclic graphical models that can effectively characterize and analyze uncertainty, which is a problem commonly encountered in real-world domains, and handle state space explosion problems [3]. The applications of BNs has been extended to many fields involving uncertainty [4], from risk analysis [5,6], safety engineering [7], resilience engineering [8], and fault diagnosis [9][10][11] to current reliability engineering, which is mainly discussed in the present work. BN-based reliability evaluation is conducted by forward (or predictive) analysis of BNs with various inference algorithms.…”