This paper presents a framework to compare the resiliency of different designs during the conceptual design, when information about implementation details is unavailable. We apply the Inherent Behavioral Functional Model (IBFM) tool to develop an initial functional model for a system and simulate the failure behavior. The simulated failure scenarios provide us the information on the unique failure propagation paths and the end state/final behavior of the system assigned to each failure. Each failure path is caused by injecting one or multiple simultaneous faults into the functional model. Within this framework, we generate a population of functional models from a baseline seed model, and evaluate its potential failure scenarios. We also develop a cost-risk model to compare resiliency of different designs, and produce a preference ranking. select the most resilient one, based upon the cost-risk objective. The risk is calculated based on the probability of having an undesired end state for each design, and a consequential cost is assigned to each failure to quantify the cost-risk for a given design. In this paper, we implement and demonstrate the proposed method on the design of a resilient mono-propellant system.

This paper represents a step toward a more complete frame-work of safety analysis early in the design process, specifically during functional modeling. This would be especially useful when designing in a new domain, where many functions have yet to be solved, or for a problem where the functional architecture space is large. In order to effectively analyze the inherent safety of a design only described by its functions and flows, we require some way to simulate it. As an already-available function failure reasoning tool, Function Failure Identification and Propagation (FFIP) utilizes two distinct system models: a behavioral model, and a functional model. The behavioral model simulates system component behavior, and FFIP maps specific component behaviors to functions in the functional model. We have created a new function-failure reasoning method which generalizes failure behavior directly to functions, by which the engineer can create functional models to simulate the functional failure propagations a system may experience early in the design process without a separate behavioral model. We give each basis-defined function-flow element a pre-defined behavior consisting of nominal and failure operational modes, and the resultant effect each mode has on its functions connected flows. Flows are represented by a two-variable object reminiscent of a bond from bond graphs: the state of each flow is represented by an effort variable and a flow-rate variable. The functional model may be thought of as a bond graph where each functional element is a state machine. Users can quickly describe functional models with consistent behavior by constructing their models as Python NetworkX graph objects, so that they may quickly model multiple functional architectures of their proposed system. We are implementing the method in Python to be used in conjunction with other function-failure analysis tools. We also introduce a new method for the inclusion of time in a state machine model, so that dynamic systems may be modeled as fast-evaluating state machines. State machines have no inherent representation of time, while physics-based models simulate along repetitive time steps. We use a more middle-ground pseudo time approach. State transitions may impose a time delay once all of their connected flow conditions are met. Once the entire system model has reached steady state in a timeless sense, the clock is advanced all at once to the first time at which a reported delay is ended. Simulation then resumes in the timeless sense. We seek to demonstrate this modeling method on an electrical power system functional model used in previous FFIP studies, in order to compare the failure scenario results of an exhaustive fault combination experiment with similar results using the FFIP method.

Most engineered systems have to exhibit a high degree of reliability and robustness. They are high in cost and complexity and often incorporate highly sophisticated materials, components, design and other technologies. Therefore, they face uncertainties in categories ranging from technical issues to market changes. This includes a wide range of epistemic uncertainties, such as demand or budget uncertainty; due to increasingly dynamic markets it has become important for systems to cope with these uncertainties. In this paper, a Kalman filter approach is applied to control the design as uncertainties are resolved in a discrete time frame. It is shown how the Kalman filter approach treats the design as a stochastic control problem, in which the design is controlled throughout its lifecycle to compensate for sources of epistemic uncertainty, as the uncertainties are resolved. The proposed method is applicable to flexible systems where changing the design is possible. A design framework is proposed encompassing a set of definitions, metrics, the methodology, and a case study of a spaceborne system.

It is desirable for complex engineered systems to be resilient to various sources of uncertainty throughout their life cycle. Such systems are high in cost and complexity, and often incorporate highly sophisticated materials, components, design, and other technologies. There are many uncertainties such systems will face throughout their life cycles due to changes in internal and external conditions, or states of interest, to the designer, such as technology readiness, market conditions, or system health. These states of interest affect the success of the system design with respect to the main objectives and application of the system, and are generally uncertain over the life cycle of the system. To address such uncertainties, we propose a resilient design approach for engineering systems. We utilize a Kalman filter approach to model the uncertain future states of interest. Then, based upon the modeled states, the optimal change in the design of the system is achieved to respond to the new states. This resilient method is applicable in systems when the ability to change is embedded in the system design. A design framework is proposed encompassing a set of definitions, metrics, and methodologies. A case study of a communication satellite system is presented to illustrate the features of the approach.

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.