AADL-Based Stochastic Error Propagation Analysis for Reliable System Design of a Medical Patient Table
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
This paper introduces a new method for stochastic error propagation analysis of mechatronic systems designed with Architecture Analysis Design Language (AADL). The analysis is based on a formal Dual-graph Error Propagation Model (DEPM). This model captures control and data flow aspects and reliability properties of system components and allows the quantitative system reliability evaluation using underlying Markov chain models. This paper describes an automatic transformation algorithm from AADL to DEPM that identifies data and control flow transitions between devices, processes and threads of an AADL model and generates a DEPM for further error propagation analysis. An integrated third-party scheduling tool Cheddar helps to generate control flow sequences that are transformed into a stochastic control flow graph of the DEPM. The generated DEPM allows a user to specify fault activation probabilities for particular system components and numerically analyze error propagation to critical outputs. The introduced method is illustrated with a reliability analysis of a mobile medical patient table.
Details
Original language | English |
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Title of host publication | 2018 Annual Reliability and Maintainability Symposium, RAMS 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (print) | 9781538628706 |
Publication status | Published - 11 Sept 2018 |
Peer-reviewed | Yes |
Publication series
Series | Annual Symposium on Reliability and Maintainability (RAMS) |
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Volume | 2018-January |
ISSN | 2577-0993 |
Conference
Title | 2018 Annual Reliability and Maintainability Symposium, RAMS 2018 |
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Duration | 22 - 25 January 2018 |
City | Reno |
Country | United States of America |
External IDs
Scopus | 85054138194 |
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Keywords
ASJC Scopus subject areas
Keywords
- AADL, Control flow, Data flow, Error Propagation Analysis, Healthcare, Model-to-model Transformation, Reliability Modeling