Function-Based Failure Propagation for Conceptual Design

When designing a product, the earlier the potential risks can be identified, the more costs can be saved, as it is easier to modify a design in its early stages. Several methods exist to analyze the risk in a system, but all require a mature design. However, by applying the concept of “common interfaces” to a functional model and utilizing a historical knowledge base, it is possible to analyze chains of failures during the conceptual phase of product design. This paper presents a method based on these common interfaces to be used in conjunction with other methods such as risk in early design to allow a more complete risk analysis during the conceptual design phase. Finally, application of this method is demonstrated in a design setting by applying it to a thermal control subsystem

FMDTOOLS: A Fault propagation Toolkit for Resilience Assessment in Early Design

Incorporating resilience in design is important for the long-term viability of complex engineered systems. Complex aerospace systems, for example, must ensure safety in the event of hazards resulting from part failures and external circumstances while maintaining efficient operations. Traditionally, mitigating hazards in early design has involved experts manually creating hazard analyses in a time-consuming process that hinders one’s ability to compare designs. Furthermore, as opposed to reliability-based design, resilience-based design requires using models to determine the dynamic effects of faults to compare recovery schemes. Models also provide design opportunities, since models can be parameterized and optimized and because the resulting hazard analyses can be updated iteratively. While many theoretical frameworks have been presented for early hazard assessment, most currently-available modelling tools are meant for the later stages of design. Given the wide adoption of Python in the broader research community, there is an opportunity to create an environment for researchers to study the resilience of different PHM technologies in the early phases of design. This paper describes fmdtools, an attempt to realize this opportunity with a set of modules which may be used to construct different design models, simulate system behaviors over a set of fault scenarios and analyze the resilience of the resulting simulation results. This approach is demonstrated in the hazard analysis and architecture design of a multi-rotor drone, showing how the toolkit enables a large number of analyses to be performed on a relatively simple model as it progresses through the early design process

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

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 risk mitigation strategy taxonomy and generated risk event effect neutralization method

In the design of new products and systems, the mitigation of potential failures is very important. The sooner in a product\u27s design mitigation can be performed, the lower the cost and easier to implement those mitigations become. However, currently, most mitigations strategies rely on the expertise of the engineers designing a product, and while models and for failure modes do exist to help, there are no guidelines for performing product changes to reduce risk. To help alleviate this, the risk mitigation strategy taxonomy is created from an empirical collection of mitigation strategies used in industry for failure mitigation, creating a consistent set of definitions for electromechanical risk mitigation strategies. By storing mitigation data in this consistent format, the data can be used to evaluate and compare different mitigation strategies. Applying this, the Generated Risk Event Effect Neutralization (GREEN) method is used to generate mitigation strategies for a product during the conceptual design of the product, where changes are the easiest to implement and cost the least. The GREEN method then compares and selects the best strategy based on the popularity, likelihood change, and consequence change that result from implementing the strategies --Abstract, page iv

Evaluating complex engineered systems using complex network representations

This thesis is the combination of two research publications working toward a unified strategy in which to represent complex engineered systems as complex networks. Current engineered system modeling techniques segment large complex models into multiple groups to be simulated independently. These methods restrict the evaluations of such complex systems as their failure properties are typically unknown until they are experienced in operation. In an effort to combat the computationally prohibitive simulations required for the analysis of complex engineered systems, complex networks are used to simplify the analysis and provide data during early design when costs for design changes and associated risk are lower. The first publication presents a methodology in which to model complex engineered systems as networks so that nodes are commensurate in ontological category under a common analysis goal. The second publication identifies a model scaling technique in which to evaluate network topology metrics for an evaluation of parameterized failure performance. Each publication utilized a drivetrain model to illustrate and simulate the methods and potential results. It was found that a bipartite behavioral network is capable of consistently identifying system failures within network topology. By analyzing complex engineered systems with complex network techniques, an evaluation of system robustness can be developed in an effort to eliminate variation in system performance