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While many organizations claim to "put safety first," safety is rarely considered early in the design process when system-level architectural decisions are made. Instead, system design follows an abstraction-to-detail process to first meet functional and then performance requirements. Following this process, safety assurance occurs in the later stages of design through a rigorous expert review process. The significant cost of safety-based redesign and the growing complexity of engineered systems motivates a need for early design-stage fault analysis. This research presents a novel method of including safety into the model-based design and analysis of complex systems using low-fidelity behavior simulations. Specifically, this research demonstrates the adaption of the functional design process to explicitly include the system property of safety in the system representation. Next, early design fault analysis is extended to connect component failure behavior to system-level hazards. Finally, this research develops three methods of results clustering to provide different evaluation metrics of the system design. In summary, this research demonstrates a framework for incorporating safety into early design decision making. This research addresses safety and failure in the design of complex systems incorporating diverse technology domains as found in energy, transportation, and aerospace systems. / Graduation date: 2012

Traditionally design engineers have used the Factor of Safety method for ensuring that designs do not fail in the field. Access to advanced computational tools and resources have made this process obsolete and new methods to introduce higher levels of reliability in an engineering systems are currently being investigated. However, even though high computational resources are available the computational resources required by reliability analysis procedures leave much to be desired. Furthermore, the regression based surrogate modeling techniques fail when there is discontinuity in the design space, caused by failure mechanisms, when the design is required to perform under severe externalities. Hence, in this research we propose efficient Semi-Supervised Learning based surrogate modeling techniques that will enable accurate estimation of a system's response, even under discontinuity. These methods combine the available set of labeled dataset and unlabeled dataset and provide better models than using labeled data alone. Labeled data is expensive to obtain since the responses have to be evaluated whereas unlabeled data is available in plenty, during reliability estimation, since the PDF information of uncertain variables is assumed to be known. This superior performance is gained by combining the efficiency of Probabilistic Neural Networks (PNN) for classification and Expectation-Maximization (EM) algorithm for treating the unlabeled data as labeled data with hidden labels.

Component failures can result in catastrophic behaviors in safety critical embedded systems, sometimes resulting in loss of life. Component failures can be treated as off nominal behaviors (ONBs) with respect to the components and sub systems involved in an embedded system. A lot of research is being carried out to tackle the problem of ONBs. These approaches are mainly focused on the states (i.e., desired and undesired states of a system at a given point of time to detect ONBs). In this paper, an approach is discussed to detect component failures and critical components of an embedded system. The approach is based on fault tree analysis (FTA), applied to the requirements specification of embedded systems at design time to find out the relationship between individual component failures and overall system failure. FTA helps in determining both qualitative and quantitative relationship between component failures and system failure. Analyzing the system at design time helps in detecting component failures and critical components and helps in devising strategies to mitigate component failures at design time and improve overall safety and reliability of a system.