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.

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.

The Impact of Abstraction and Fidelity Levels on the Usefulness of Early System Functional Models

2016 ◽  
Author(s):  
Sean C. Hunter ◽  
David C. Jensen ◽  
Irem Y. Tumer ◽  
Christopher Hoyle
Keyword(s):  
Failure Analysis ◽  
Computational Models ◽  
Mitigation Strategies ◽  
Design Stage ◽  
Levels Of Abstraction ◽  
Early Design Stage ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Experimental Findings ◽  
The Impact

For many complex engineered systems, a risk informed approach to design is critical to ensure both robust safety and system reliability. Early identification of failure paths in complex systems can greatly reduce the costs and risks absorbed by a project in future failure mitigation strategies. By exploring the functional effect of potential failures, designers can identify preferred architectures and technologies prior to acquiring specific knowledge of detailed physical system forms and behaviors. Early design-stage failure analysis is enabled by model-based design, with several research methodologies having been developed to support this design stage analysis through the use of computational models. The abstraction necessary for implementation at the design stage, however, leads to challenges in validating the analysis results presented by these models. This paper describes initial work on the comparison of models at varying levels of abstraction with results obtained on an experimental testbed in an effort to validate a function-based failure analysis method. Specifically, the potential functional losses of a simple rover vehicle are compared with experimental findings of similar failure scenarios. Expected results of the validation procedure suggest that a model’s validity and quality are a function of the depth to which functional details are described.

Concurrent Design of Functional Reliability and Failure Prognosis for Engineered Resilience

2013 ◽  
Author(s):  
Pingfeng Wang ◽  
Byeng D. Youn ◽  
Chao Hu
Keyword(s):  
Life Cycle ◽  
System Design ◽  
Design Stage ◽  
System Failures ◽  
Complex Engineered Systems ◽  
Functional Reliability ◽  
Use Phase ◽  
Engineered Systems ◽  
Failure Prognosis ◽  
System Life

This paper presents a new system design platform and approaches leading to the development of resilient engineered systems through integrating design of system functions and prognosis of function failures in a unified design framework. Failure prognosis plays an increasingly important role in complex engineered systems since it detects, diagnoses, and predicts the system-wide effects of adverse events, therefore enables a proactive approach to deal with system failures at the life cycle use phase. However, prognosis of system functional failures has been largely neglected in the past at early system design stage, mainly because quantitative analysis of failure prognosis in the early system design stage is far more challenging than these activities themselves that have been mainly carried out at the use phase of a system life cycle. In this paper, a generic mathematical formula of resilience and predictive resilience analysis will be introduced, which offers a unique way to consider lifecycle use phase failure prognosis in the early system design stage and to systematically analyze their costs and benefits, so that it can be integrated with system function designs concurrently to generate better overall system designs. Engineering design case studies will be used to demonstrate the proposed design for resilience methodology.

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.

Model Validation in Early Phase of Designing Complex Engineered Systems

2018 ◽  
Author(s):  
Elham Keshavarzi ◽  
Kai Goebel ◽  
Irem Y. Tumer ◽  
Christopher Hoyle
Keyword(s):  
Model Simulation ◽  
Early Stage ◽  
Design Stage ◽  
Early Design ◽  
The Real ◽  
Early Design Stage ◽  
Complex Engineered Systems ◽  
Search Tool ◽  
Engineered Systems

In design process of a complex engineered system, studying the behavior of the system prior to manufacturing plays a key role to reduce cost of design and enhance the efficiency of the system during its lifecycle. To study the behavior of the system in the early design phase, it is required to model the characterization of the system and simulate the system’s behavior. The challenge is the fact that in early design stage, there is no or little information from the real system’s behavior, therefore there is not enough data to use to validate the model simulation and make sure that the model is representing the real system’s behavior appropriately. In this paper, we address this issue and propose methods to validate the model developed in the early design stage. First we propose a method based on FMEA and show how to quantify expert’s knowledge and validate the model simulation in the early design stage. Then, we propose a non-parametric technique to test if the observed behavior of one or more subsystems which currently exist, and the model simulation are the same. In addition, a local sensitivity analysis search tool is developed that helps the designers to focus on sensitive parts of the system in further design stages, particularly when mapping the conceptual model to a component model. We apply the proposed methods to validate the output of failure simulation developed in the early stage of designing a monopropellant propulsion system design.

Generalised DOPs with Consideration of the Influence Function of Signal-in-Space Errors

2011 ◽  
Vol 64 (S1) ◽  
pp. S3-S18 ◽  
Author(s):  
Yuanxi Yang ◽  
Jinlong Li ◽  
Junyi Xu ◽  
Jing Tang
Keyword(s):  
Least Squares ◽  
Stochastic Models ◽  
Influence Function ◽  
A Priori ◽  
Functional Model ◽  
Global Navigation Satellite Systems ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Integrated Navigation ◽  
Satellite Systems

Integrated navigation using multiple Global Navigation Satellite Systems (GNSS) is beneficial to increase the number of observable satellites, alleviate the effects of systematic errors and improve the accuracy of positioning, navigation and timing (PNT). When multiple constellations and multiple frequency measurements are employed, the functional and stochastic models as well as the estimation principle for PNT may be different. Therefore, the commonly used definition of “dilution of precision (DOP)” based on the least squares (LS) estimation and unified functional and stochastic models will be not applicable anymore. In this paper, three types of generalised DOPs are defined. The first type of generalised DOP is based on the error influence function (IF) of pseudo-ranges that reflects the geometry strength of the measurements, error magnitude and the estimation risk criteria. When the least squares estimation is used, the first type of generalised DOP is identical to the one commonly used. In order to define the first type of generalised DOP, an IF of signal–in-space (SIS) errors on the parameter estimates of PNT is derived. The second type of generalised DOP is defined based on the functional model with additional systematic parameters induced by the compatibility and interoperability problems among different GNSS systems. The third type of generalised DOP is defined based on Bayesian estimation in which the a priori information of the model parameters is taken into account. This is suitable for evaluating the precision of kinematic positioning or navigation. Different types of generalised DOPs are suitable for different PNT scenarios and an example for the calculation of these DOPs for multi-GNSS systems including GPS, GLONASS, Compass and Galileo is given. New observation equations of Compass and GLONASS that may contain additional parameters for interoperability are specifically investigated. It shows that if the interoperability of multi-GNSS is not fulfilled, the increased number of satellites will not significantly reduce the generalised DOP value. Furthermore, the outlying measurements will not change the original DOP, but will change the first type of generalised DOP which includes a robust error IF. A priori information of the model parameters will also reduce the DOP.

A Performance Modeling Language for Automotive Embedded Control Systems Based on UML

2012 ◽  
Vol 236-237 ◽  
pp. 344-349
Author(s):  
Xiao Feng Yin ◽  
Jing Xing Tan ◽  
Xiu Ting Wu ◽  
Zhi Jun Gong
Keyword(s):  
Performance Modeling ◽  
Resource Constraints ◽  
Timing Analysis ◽  
Unified Modeling Language ◽  
Functional Model ◽  
Design Stage ◽  
Modeling Language ◽  
Embedded Control ◽  
Early Design Stage ◽  
A Performance

To improve the timing related performance of the embedded software of automotive control system, a performance modeling language has been developed based on UML (Unified Modeling Language) using meta-modeling technique. The proposed language consists of three kinds of meta-models used to define the high-level modeling paradigms for software structure, target platform and runtime system respectively. The modeling environment configured by the proposed language and software modules of functional model importation, components allocation, task forming and timing analysis can reuse the existing functional models, add timing requirement as well as resource constraints, and fulfill formal timing analysis at an early design stage. As results, the reliability of the automotive embedded control software can be improved and the development cycle and cost can also be reduced.

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.

Sign in / Sign up

Export Citation Format

Share Document