Risk Assessment

Rausand, Marvin; Haugen, Stein

doi:10.1002/9781119377351

Cited by 79 publications

(39 citation statements)

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“…A challenge with this approach is that it is not clear from the network which failure causes that should be considered when quantifying the CPTs. A hazard analysis can be performed to identify these causes [28], [29], which can help with quantification.…”

Section: Discussionmentioning

confidence: 99%

“…Damage to the robotic system is one example of a loss. Relevant hazards can be identified through different risk analysis methods, such as preliminary hazard analysis (PHA) [29] or system theoretic process analysis (STPA) [28]. A node is introduced in the DDN for every goal and hazard.…”

Section: Methodsmentioning

confidence: 99%

“…Not achieving an objective is considered a failure. The failure causes can be identified with a risk analysis; see [28], [29]. Intermediate nodes are introduced to group all failure causes that affect the same set of objectives.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian Approach to Risk-Based Autonomy for a Robotic System Executing a Sequence of Independent Tasks

Rothmund¹,

Thieme²,

Utne³

et al. 2021

Preprint

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Enabling higher levels of autonomy requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This work presents an approach to improve this ability for a robotic system executing a series of independent tasks, such as inspection, sampling, or intervention, at different locations. A dynamic decision network (DDN) is used to infer the presence of internal faults and the state of the environment by fusing information over time. This knowledge is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Past states are evaluated with new information to identify and counteract previous sub-optimal actions. A case study on an inspection drone tasked with contact-based ultrasound inspection is presented. The case study successfully demonstrates the proposed capabilities while minimizing time use and maximizing mission completion.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian Approach to Risk-Based Autonomy for a Robotic System Executing a Sequence of Independent Tasks

Rothmund¹,

Thieme²,

Utne³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Such failures can disturb the functionality of systems, thus affecting their reliability. Modelling dependencies of CIs in order to understand the behaviour of complex systems encountered with failures that may be caused by adversaries is a common approach towards enhancing the reliability of systems [ 24 , 25 ]. In general, modelling CIs in terms of their interdependencies provides an insightful view of inter-system and intra-system causal relationships, response behaviour, failure types, state of operation, and risks that arise due to the dependency-related failures in systems [ 26 , 27 ].…”

Section: Related Workmentioning

confidence: 99%

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

Akbarzadeh

Katsikas

2021

Sensors

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Contemporary Critical Infrastructures (CIs), such as the power grid, comprise cyber physical systems that are tightly coupled, to form a complex system of interconnected components with interacting dependencies. Modelling methodologies have been suggested as proper tools to provide better insight into the dependencies and behavioural characteristics of these complex systems. In order to facilitate the study of interconnections in and among critical infrastructures, and to provide a clear view of the interdependencies among their cyber and physical components, this paper proposes a novel method, based on a graphical model called Modified Dependency Structure Matrix (MDSM). The MDSM provides a compact perspective of both inter-dependency and intra-dependency between subsystems of one complex system or two distinct systems. Additionally, we propose four parameters that allow the quantitative assessment of the characteristics of dependencies, including multi-order dependencies in large scale CIs. We illustrate the workings of the proposed method by applying it to a micro-distribution network based on the G2ELAB 14-Bus model. The results provide valuable insight into the dependencies among the network components and substantiate the applicability of the proposed method for analyzing large scale cyber physical systems.

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“…Risk is often associated with negative outcomes, however, may include positive outcomes. Safety of an operation or a system is established if the risk has been reduced to an acceptable and tolerable level 8 . That means that measures have been taken to reduce the frequency or probability of occurrence of consequences and to mitigate the impact of negative consequences.…”

Section: Introductionmentioning

confidence: 99%

Risk-informed control systems for improved operational performance and decision-making

Thieme

Rokseth

Utne

2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Autonomous systems, including airborne, land-based, marine, and underwater vehicles, are increasingly present in the world. One important aspect of autonomy is the capability to process information and to make independent decisions for achieving a mission goal. Information on the level of risk related to the operation may improve the decision-making process of autonomous systems. This article describes the integration of risk analysis methods with the control system of autonomous and highly automated systems that are evaluated during operation. Four main areas of implementation are identified; (i) risk models used to directly make decisions, (ii) use of the output of risk models as input to decision-making and optimization algorithms, (iii) the output of risk models may be used as a constraint in or modifying constraints of algorithms, and (iv) the output of risk models may be used to inform representations or maps of the environment to be used in path planning. A case study on a dynamic positioning controller of an offshore supply vessel exemplifies the concepts described in this article. In addition, it demonstrates how risk model output may be used within a hybrid controller.

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Risk Assessment

Cited by 79 publications

References 0 publications

A Bayesian Approach to Risk-Based Autonomy for a Robotic System Executing a Sequence of Independent Tasks

A Bayesian Approach to Risk-Based Autonomy for a Robotic System Executing a Sequence of Independent Tasks

Identifying and Analyzing Dependencies in and among Complex Cyber Physical Systems

Risk-informed control systems for improved operational performance and decision-making

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