Improved Dynamic Dependability Assessment Through Integration With Prognostics

Abstract

This version is available at https://strathprints.strath.ac.uk/60422/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract-The use of average data for dependability assessments results in a outdated system-level dependability estimation which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field which provides asset-specific failure information which can be reused to improve the system level failure estimation. This paper presents a framework for prognosticsupdated dynamic dependability assessment. The dynamic behaviour comes from runtime updated information, asset interdependencies, and time-dependent system behaviour. A case study from the power generation industry is analysed and results confirm the validity of the approach for improved near real-time unavailability estimations.

A Data Mining Approach for Transformer Failure Rate Modeling Based on Daily Oil Chromatographic Data

“…[28] established an uncertainty-aware dynamic reliability analysis framework without the need of exact failure data of its components. [29] presented a framework for prognostics-updated dynamic dependability assessment based on the online data analysis. [30] used the FTA for performing reliability analysis of an management system.…”

Dynamic business continuity assessment using condition monitoring data

“…On the other hand, the advancing of sensor technologies and computing resources has made it possible to retrieve information on the state of components and systems, by collecting and elaborating condition monitoring data [27,28]. For example, a condition-based fault tree has been used for dynamic risk assessment (DRA) [29], where the condition monitoring data are used to update the failure rates of specific components and predict their reliability. In [30], a Bayesian reliability updating method has been developed for dependent components by using condition monitoring data.…”

A framework for dynamic risk assessment with condition monitoring data and inspection data

“…Condition monitoring data refer to the online monitoring data collected by sensors that are installed in the target system for monitoring the degradation process of the safety barrier. For example, a condition-based fault tree has been used for DRA, where the condition monitoring data have been used to update the failure rates of the specific components and predict the reliability [25,26]. Particle filtering (PF) has been used for DRA based on condition monitoring data from a nonlinear non-Gaussian process [27].…”