Functional Models With Inherent Behavior: Towards a Framework for Safety Analysis Early in the Design of Complex Systems

McIntire, Matthew G.; Keshavarzi, Elham; Tumer, Irem Y.; Hoyle, Christopher

doi:10.1115/imece2016-67040

Cited by 13 publications

(9 citation statements)

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“…Other methods have been created to focus on the propagation of failures through a functional model [15]. Inherent Behavior of Functional Models (IBFM), which is used in this paper, provided a method to automate the creation of a state-based behavior model from the functional model itself [16].…”

Section: Prior Workmentioning

confidence: 99%

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

Hulse

Hoyle

Goebel

et al. 2018

Volume 2A: 44th Design Automation Conference

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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.

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Section: Prior Workmentioning

confidence: 99%

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

Hulse

Hoyle

Goebel

et al. 2018

Volume 2A: 44th Design Automation Conference

Self Cite

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show abstract

“…This paper uses the IBFM modeling language to simulate the fault propagation behavior of a system given its functional model. IBFM was developed for this purpose, with a focus on speed and ability to simulate large sets of joint-fault scenarios [33]. This tool acts by constructing a behavioral model from the functional model by associating behaviors with functions and states with flows.…”

Section: Inherent Behavior Of Functional Models Simulation Andmentioning

confidence: 99%

“…5: the designer defines an initial functional model and creates a behavioral model by associating conditions, behaviors, and modes with the various functions as presented in Ref. [33] using IBFM. The designer then associates costs and probabilities with the various modes, functions, and flow states, and defines the changes to be explored within a parameterized cost scoring.…”

Section: Optimizationmentioning

confidence: 99%

“…The function failure identification and propagation method (FFIP) informs assessment by constructing a graph-based behavioral model to take into account the function interactions, dynamics, and joint fault scenarios [8] which has since been extended using flow-state logic to model undesired flow states [29] and dimensional analysis to incorporate more detailed information about component behavior [30] and adapted for large-scale complex systems [31] and mechatronics [32]. Inherent behavior of functional models (IBFM), which is used in this paper, provided a method to automate the creation of a state-based behavior model from the functional model itself [33]. Approaches have additionally been presented, which associate the functional model with a fault tree [34][35][36], and other methods have been created to focus on the propagation of failures through a functional model [37].…”

Section: Introductionmentioning

confidence: 99%

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

Hulse

Hoyle

Goebel

et al. 2018

Journal of Mechanical Design

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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.

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“…Several research works have been dedicated to developing diagnostic methods for various faults in the aircraft EPS. The Advanced Diagnostic and Prognostic Testbed (ADAPT), an EPS testbed developed by NASA Ames Research Centre, has been used in multiple scenarios to evolve diagnosis and prognosis technologies [6][7][8][9]. ADAPT evaluates diagnostic algorithms and software, enables the insertion of faults, and provides a mechanism for evolving the algorithms to be implemented into the vehicles [10].…”

Section: Introductionmentioning

confidence: 99%

A System-Level Failure Propagation Detectability Using ANFIS for an Aircraft Electrical Power System

Ezhilarasu

Jennions

2020

Applied Sciences

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The Electrical Power System (EPS) in an aircraft is designed to interact extensively with other systems. With a growing trend towards more electric aircraft, the complexity of interactions between the EPS and other systems has grown. This has resulted in an increased necessity of implementing health monitoring methods like diagnosis and prognosis of the EPS at the systems level. This paper focuses on developing a diagnostic algorithm for the EPS to detect and isolate faults and their root causes that occur at the Line Replaceable Units (LRUs) connecting with aircraft systems like the engine and the fuel system. This paper aims to achieve this in two steps: (i) developing an EPS digital twin and presenting the simulation results for both healthy and fault scenarios, (ii) developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) monitor to detect faults in the EPS. The results from the ANFIS monitor are processed in two methods: (i) a crisp boundary approach, and (ii) a fuzzy boundary approach. The former approach has a poor misclassification rate; hence the latter method is chosen to combine with causal reasoning for isolating root causes of these interacting faults. The results from both these methods are presented through examples in this paper.

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Functional Models With Inherent Behavior: Towards a Framework for Safety Analysis Early in the Design of Complex Systems

Cited by 13 publications

References 0 publications

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

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

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

A System-Level Failure Propagation Detectability Using ANFIS for an Aircraft Electrical Power System

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