System Level Risk Analysis for Immunity in Automotive Functional Safety Analyses

Devaraj, Lokesh; Ruddle, Alastair R.; Duffy, Alistair

doi:10.1109/emceurope48519.2020.9245692

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…PRACTICAL APPLICATION ILLUSTRATIONS Graphical models offer the advantage of improved visualization for complicated systems, and increased scope and granularity can be achieved by simply adding further relevant nodes and edges to the model. More importantly, PGMs can also be employed for estimating answers to probabilistic queries relating to the node variables of the model, as discussed in [2].…”

Section: Model Structurementioning

confidence: 99%

“…Moreover, due to technical and economic limitations, it is not practicable to test the performance of all possible states of a complex system and its functions over the entire EM spectrum and modulations. These concerns have motivated interest in adopting a risk-based approach as an alternative to the existing prescriptive rule-based approach [1]- [2].…”

Section: Introductionmentioning

confidence: 99%

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Risk Assessment Approach for EM Resilience in Complex Systems Using Bayesian Networks

Devaraj

Ruddle

Duffy

et al. 2021

2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium

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Current trends in the automotive industry are reshaping the architectures and electromagnetic characteristics of road vehicles. Increasing electrification and connectivity are enabling considerable packaging flexibility and leading to radically different electromagnetic environments. At the same time, increasing automation of driving functions will require unprecedented levels of system dependability. However, existing EMC engineering processes were developed in a very different world of low system complexity and incremental technological development. In order to adapt to rising system complexity and the increasingly rapid pace of technological change, it is considered that a more agile risk-based approach is better suited to ensure the electromagnetic resilience of future vehicles and other complex systems. This paper outlines a Bayesian network approach that allows the combination of both technical and nontechnical aspects in assessing the likelihood of issues that could lead to system-level risks. This approach could be used from the earliest stages of product development, where the detailed information required to undertake detailed risk assessment is generally unavailable.

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Section: Model Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk Assessment Approach for EM Resilience in Complex Systems Using Bayesian Networks

Devaraj

Ruddle

Duffy

et al. 2021

2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium

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“…safetyrelated) can be taken with informed risk levels relating to possible system level hazards. To identify the tools required to support a risk-based systemlevel electromagnetic (EM) approach for func-tional safety, the limitations of traditional tools like fault tree analysis, event tree analysis and bow-tie analysis, and the benefits of probabilistic graphical models (PGMs) like Bayesian Networks (BN) and Markov random fields, are already discussed in Devaraj et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Approach for System Level Electromagnetic Safety Analysis

Devaraj¹,

Ruddle²,

Khan³

et al. 2021

Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)

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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.

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“…To achieve EMC for modern complex systems with rapid advancement in the features and functionality (like automated driving in road vehicles), the current industrial practice of rule-based EMC testing approach needs to be shifted, more towards a risk-based approach. As also discussed in [1], in order to accomplish a risk-based electromagnetic (EM) approach, hazards identification and risk estimation activities should commence from the initial concept phase of the system itself, and should be continuously refined as more and more system knowledge is obtained.…”

Section: Introductionmentioning

confidence: 99%

EMI Risk Estimation for System-Level Functions Using Probabilistic Graphical Models

Devaraj

Ruddle

Duffy

2021

2021 IEEE International Joint EMC/SI/PI and EMC Europe Symposium

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In general, the functions provided by complex systems often involve multiple sub-systems and components that are functionally dependent on each other. The dependency could be to receive power, control signals, input data, memory storage, feedback etc. With the increasing use of electronic systems to perform critical functions, the potential for malfunctions due to electromagnetic interference need to be identified and mitigated. Hence, a risk analysis, estimating the likelihood and severity of electromagnetic interference effects, is desirable from the very early stages of system development. In this paper, the use of probabilistic graphical models for estimating the likelihood of electromagnetic disturbances causing system malfunctions with various degrees of severity is demonstrated using a very simple case study. Statistical data are synthesised to illustrate the construction of conditional probability distribution tables for a Bayesian Network system model. Factorization and inference techniques are then applied to demonstrate the formulation and answer of queries that could be of value during system risk assessment.

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System Level Risk Analysis for Immunity in Automotive Functional Safety Analyses

Cited by 8 publications

References 10 publications

Risk Assessment Approach for EM Resilience in Complex Systems Using Bayesian Networks

Risk Assessment Approach for EM Resilience in Complex Systems Using Bayesian Networks

Knowledge-Based Approach for System Level Electromagnetic Safety Analysis

EMI Risk Estimation for System-Level Functions Using Probabilistic Graphical Models

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