Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Daniel Hulse ◽  
Christopher Hoyle ◽  
Kai Goebel ◽  
Irem Y. Tumer
Keyword(s):  
Functional Model ◽  
Scoring Function ◽  
Modeling Languages ◽  
Concept Selection ◽  
Complex Engineered Systems ◽  
Propagation Behavior ◽  
Engineered Systems ◽  
Selection Framework ◽  
And Function ◽  
Probability Information

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience—the ability to avoid or quickly recover from faults—is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.

Optimizing Function-Based Fault Propagation Model Resilience Using Expected Cost Scoring

2018 ◽  
Author(s):  
Daniel Hulse ◽  
Christopher Hoyle ◽  
Kai Goebel ◽  
Irem Y. Tumer
Keyword(s):  
A Priori ◽  
Functional Model ◽  
Scoring Function ◽  
Propagation Model ◽  
Design Stage ◽  
Fault Propagation ◽  
Large Space ◽  
Design Work ◽  
Complex Engineered Systems ◽  
Engineered Systems

Complex engineered systems are often associated with risk due to high failure consequences, high complexity, and large investments. As a result, it is desirable for complex engineered systems to be resilient such that they can avoid or quickly recover from faults. Ideally, this should be done at the early design stage where designers are most able to explore a large space of concepts. Previous work has shown that functional models can be used to predict fault propagation behavior and motivate design work. However, little has been done to formally optimize a design based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. This work introduces a scoring function which integrates with a fault scenario-based simulation to enable the risk-neutral optimization of functional model resilience. This scoring function accomplishes this by resolving the tradeoffs between the design costs, operating costs, and modeled fault response of a given design in a way that may be parameterized in terms of designer-specified resilient features. This scoring function is adapted and applied to the optimization of controlling functions which recover flows in a monopropellant orbiter. In this case study, an evolutionary algorithm is found to find the optimal logic for these functions, showing an improvement over a typical a-priori guess by exploring a large range of solutions, demonstrating the value of the approach.

Toward an Automated Model-Based Geometric Method of Representing Function Failure Propagation Across Uncoupled Systems

2014 ◽  
Author(s):  
Isaac J. Ramp ◽  
Douglas L. Van Bossuyt
Keyword(s):  
Functional Model ◽  
Geometric Method ◽  
Flow Paths ◽  
Functional Failure ◽  
Model Based ◽  
Complex Engineered Systems ◽  
Minimum Number ◽  
Failure Propagation ◽  
Functional Layout ◽  
Engineered Systems

The complex engineered systems being designed today must rapidly and accurately be developed to satisfy customer needs while accomplishing required functions with a minimum number of failures. Failure analysis in the conceptual stage of design has expanded in recent years to account for failures in functional modeling. However, function failure propagation across normally uncoupled functions and subsystems has not been fully addressed. A functional model-based geometric method of predicting and mitigating functional failure propagation across systems, which are uncoupled during nominal use cases, is presented. Geometric relationships between uncoupled functions are established to serve as failure propagation flow paths. Mitigation options are developed based upon the geometric relationships and a path toward physical functional layout is provided to limit failure propagation across uncoupled subsystems. The model-based geometric method of predicting and mitigating functional failure propagation across uncoupled engineered systems guides designers toward improved protection and isolation of cross-subsystem failure propagation.

scholarly journals Forensic Engineering Use Of Fault Tree Analysis

2006 ◽  
Vol 23 (2) ◽  
Author(s):  
Frank H. Johnson ◽  
DeWitt William E.
Keyword(s):  
Fault Tree ◽  
Fault Tree Analysis ◽  
Tree Analysis ◽  
Complex Engineered Systems ◽  
Track Record ◽  
Forensic Engineering ◽  
Fire And Explosion ◽  
Engineered Systems ◽  
Analytical Tools

Analytical Tools, Like Fault Tree Analysis, Have A Proven Track Record In The Aviation And Nuclear Industries. A Positive Tree Is Used To Insure That A Complex Engineered System Operates Correctly. A Negative Tree (Or Fault Tree) Is Used To Investigate Failures Of Complex Engineered Systems. Boeings Use Of Fault Tree Analysis To Investigate The Apollo Launch Pad Fire In 1967 Brought National Attention To The Technique. The 2002 Edition Of Nfpa 921, Guide For Fire And Explosion Investigations, Contains A New Chapter Entitled Failure Analysis And Analytical Tools. That Chapter Addresses Fault Tree Analysis With Respect To Fire And Explosion Investigation. This Paper Will Review The Fundamentals Of Fault Tree Analysis, List Recent Peer Reviewed Papers About The Forensic Engineering Use Of Fault Tree Analysis, Present A Relevant Forensic Engineering Case Study, And Conclude With The Results Of A Recent University Study On The Subject.

scholarly journals Lower complexity bounds for lifted inference

2014 ◽  
Vol 15 (2) ◽  
pp. 246-263 ◽  
Author(s):  
MANFRED JAEGER
Keyword(s):  
Knowledge Bases ◽  
Modeling Languages ◽  
Complexity Bounds ◽  
Lifted Inference ◽  
Probabilistic Relational Models ◽  
Lower Complexity ◽  
Logical Modeling ◽  
Inference Techniques ◽  
And Function ◽  
Lower Complexity Bounds

AbstractOne of the big challenges in the development of probabilistic relational (or probabilistic logical) modeling and learning frameworks is the design of inference techniques that operate on the level of the abstract model representation language, rather than on the level of ground, propositional instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about probabilistic relational models. Artificial Intelligence 117, 297–308). However, it is not immediate that these results also apply to the type of modeling languages that currently receive the most attention, i.e., weighted, quantifier-free formulas. In this paper we extend these earlier results, and show that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing the equality predicate.

scholarly journals Design of Complex Engineered Systems

2010 ◽  
Vol 132 (12) ◽  
Author(s):  
Christina L. Bloebaum ◽  
Anna-Maria R. McGowan
Keyword(s):  
Complex Engineered Systems ◽  
Engineered Systems

A Decision-Analytic Concept Selection for a Commercial System Using Objective Data to Model Uncertainty

2009 ◽  
Author(s):  
Shun Takai ◽  
Swithin S. Razu ◽  
Karan Banga
Keyword(s):  
Decision Making ◽  
Decision Analysis ◽  
Uncertainty Modeling ◽  
Case Based Reasoning ◽  
Objective Data ◽  
Concept Selection ◽  
Commercial System ◽  
System Concept ◽  
Cost Distribution ◽  
Selection Framework

This paper presents decision-analytic concept selection framework for a commercial system and an uncertainty modeling using objective data. The selection of a system concept for which a final system is designed and manufactured is a decision making process with incomplete information. Decision analysis is a prescriptive approach for decision making under uncertainty. While realizing that humans make decisions violating the expected utility axioms, decision analysis uses a set of tools to guide a decision maker toward an unbiased and rational decision making. The objective of this research is to propose a decision-analytic framework for commercial system concept selection, and an approach to utilize as much objective data as possible in the uncertainty modeling. Toward this objective, this paper construct cost distribution using case-based reasoning and market share distribution applying bootstrap to customers’ preference data obtained from conjoint analysis. The proposed approach is demonstrated in an illustrative example: a decision-analytic automobile concept selection.

Evaluation of System Reconfigurability Based on Usable Excess

2014 ◽  
Author(s):  
Jeffrey D. Allen ◽  
Jason D. Watson ◽  
Christopher A. Mattson ◽  
Scott M. Ferguson
Keyword(s):  
Communication Systems ◽  
Assembly Line ◽  
Assembly Lines ◽  
Automated Assembly ◽  
Complex Engineered Systems ◽  
Future Needs ◽  
Key Factor ◽  
Long Time ◽  
Engineered Systems ◽  
Over Time

The challenge of designing complex engineered systems with long service lives can be daunting. As customer needs change over time, such systems must evolve to meet these needs. This paper presents a method for evaluating the reconfigurability of systems to meet future needs. Specifically we show that excess capability is a key factor in evaluating the reconfigurability of a system to a particular need, and that the overall system reconfigurability is a function of the system’s reconfigurability to all future needs combined. There are many examples of complex engineered systems; for example, aircraft, ships, communication systems, spacecraft and automated assembly lines. These systems cost millions of dollars to design and millions to replicate. They often need to stay in service for a long time. However, this is often limited by an inability to adapt to meet future needs. Using an automated assembly line as an example, we show that system reconfigurability can be modeled as a function of usable excess capability.

Creating Faultable Network Models of Complex Engineered Systems

2014 ◽  
Author(s):  
Brandon M. Haley ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Network Models ◽  
System Behavior ◽  
Levels Of Abstraction ◽  
Complex Engineered Systems ◽  
System A ◽  
Topological Configuration ◽  
Engineered Systems ◽  
Types Of Faults ◽  
Insight Into

This paper presents a new methodology for modeling complex engineered systems using complex networks for failure analysis. Many existing network-based modeling approaches for complex engineered systems “abstract away” the functional details to focus on the topological configuration of the system and thus do not provide adequate insight into system behavior. To model failures more adequately, we present two types of network representations of a complex engineered system: a uni-partite architectural network and a weighted bi-partite behavioral network. Whereas the architectural network describes physical inter-connectivity, the behavioral network represents the interaction between functions and variables in mathematical models of the system and its constituent components. The levels of abstraction for nodes in both network types affords the evaluation of failures involving morphology or behavior, respectively. The approach is shown with respect to a drivetrain model. Architectural and behavioral networks are compared with respect to the types of faults that can be described. We conclude with considerations that should be employed when modeling complex engineered systems as networks for the purpose of failure analysis.

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