Knowledge-Based Approach for System Level Electromagnetic Safety Analysis
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Date
2021-09-19
Authors
Devaraj, Lokesh
Ruddle, Alastair
Khan, Qazi Mashaal
Duffy, Alistair
Journal Title
Journal ISSN
ISSN
DOI
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Publisher
Peer reviewed
Yes
Abstract
Road vehicles and similarly complex systems are constructed by integrating many subsystems and components that
are sourced from a large number of suppliers. This process may lead to the emergence of possible system-level safety
issues, some of which could be caused by external or internal electromagnetic interference. Assurance of safety by
demonstrating compliance with standard tests is becoming increasingly challenging as system complexity rises.
This is due to the costs and practical limitations of both component and system-level electromagnetic compatibility
testing. Hence, there is a need for additional methods to help estimate the likelihood of electromagnetic interference
risks associated with such systems. Probabilistic graphical models, such as Bayesian and Markov networks, are able
to provide a better visualization of various features and their relationships in a single graphical structure. Moreover,
using template models, a general-purpose representation for various integrated components of a system can be
developed for collective inference. Using such methods, this paper proposes a knowledge-based approach to assist
risk management in system-level electromagnetic engineering. The purpose of using a knowledge-based approach is
to be able to undertake safety risk analyses during the early stages of design, when many factors (e.g. internal, and
external electromagnetic interference levels, physical location of the component) remain uncertain.
Description
Keywords
Safety, Bayesian networks, risk analysis, template models, knowledge-based methods, EMC
Citation
Devaraj, L., Ruddle, A.R., Khan, Q.M., Duffy, A.P. (2021) Knowledge-Based Approach for System Level Electromagnetic Safety Analysis. 31st Annual European Safety and Reliability Conference, Angers, France, September 2021.
Research Institute
Institute of Engineering Sciences (IES)