The science of system safety provides a structured guideline for managers to follow in order to ensure safe operations, but it does not ensure against deviations from such guidelines. This responsibility lies with management. Engineering managers must be able to dictate and track safety requirements throughout product development, deployment, and operation by treating system safety as an integrated engineering discipline. It is not feasible to expect the technical teams to integrate safety into designs unless safety requirements are considered a design metric just as cost and performance. Therefore, the traditional method of employing a separate safety department to address safety requirements is not sufficient. This responsibility must be given to all technical departments and levied as a design requirement. / text

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Thesis (Ph. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; 1 v. (various pagings) : ill. (some col.). Vita. Includes abstract. Includes bibliographical references.

This thesis aims to solicit and elicit the view of experienced system safety analysts in the applicability and use of STPA, a hazard analysis derived from the STAMP-framework. The increase in complexity in systems elevates the chance of hazards and risks being obfuscated. Thus, the intention is to expand, deepen and theorize about the STPA-methodology in relation to the role of system safety analysts in Sweden. The results show that the greatest use of STPA might lie in integrating the desired procedural steps with the hazard analysis techniques used today. This is due to individual capabilities, guidance in identification and evaluation of risks, as well as the reductionistic perspective that prevails in society today. Unlike STPA’s claim for completeness, the impression of the system analysts is that absolute safety can never be guaranteed.

An approach for robust design based on stochastic expansions is investigated. The research consists of two parts : 1) stochastic expansions for uncertainty propagation and 2) adaptive sampling for Pareto front approximation. For the first part, a strategy based on the generalized polynomial chaos (gPC) expansion method is developed. Second, in order to alleviate the computational cost of approximating the Pareto front, two strategies based on adaptive sampling for multi-objective problems are presented. The first one is based on the two aforementioned methods, whereas the second one considers, in addition, two levels of fidelity of the uncertainty propagation method.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 231-238).

<p>Space habitats will involve a complex and tightly coupled combination of hardware, software, and humans, while operating in challenging environments that pose many risks, both known and unknown. It will not be possible to design habitats that are immune to failure, nor will it be possible to foresee all possible failures. Rather than aiming for designs where “failure is not an option”, habitats must be resilient to disruptions. We propose a control-theoretic approach to resilient design for space habitats based on the concept of safety controls from system safety engineering. We model disruptions using a state and trigger model, where the space habitat is in one of three distinct states at each time instance: nominal, hazardous, or accident. The habitat transitions from a nominal state to hazardous states via disruptions, and further to hazardous and accident states via triggers. We develop an approach for identifying safety controls that considers these disruptions, hazardous states, and identifies control principles and their possible control flaws. We use safety controls as ways of preventing a system from entering or remaining in a hazardous or accident state. We develop a safety control option space for the habitat, from which designers can select the set of safety controls that best meet resilience, performance, and other system goals. We show how our approach for identifying safety controls drives our control-theoretic approach for resilient design, and how that fits into the larger system safety engineering process. To identify and assess hazards, we use a database and create a network format that stores the relationships between different disruptions and hazardous states for an example space habitat. We use this database in combination with traditional hazard assessment techniques to prioritize control of possible disruptions and hazardous states. To mitigate hazards, we develop a safety control option space that contains safety controls that either prevent transition to hazardous states or return the habitat to a nominal state. We use generic safety controls, or the principle of control, to generate new safety controls as our set of disruptions and hazardous states grows, and store these in the database. Lastly, we evaluate our mitigation techniques using our control effectiveness metric, a metric intended to assess how well a safety control addresses the hazardous state or disruption that it is designed for. Our control-theoretic approach is one way in which we can complete the system safety engineering process for a space habitat system and can provide design guidance for the development of resilient space habitats.</p>

Cyber-Physical Systems represent a critical intersection of physical infrastructure and digital technologies. Ensuring the safety and reliability of these interconnected systems is vital for mitigating risks and enhancing overall system safety. In recent decades, the transportation domain has seen significant adoption of cyber-physical systems, such as automated vehicles. This dissertation will focus on the application of cyber-physical systems in transportation. Statistical learning techniques offer a powerful approach to analyzing complex transportation data, providing insights that enhance safety measures and operational efficiencies. This dissertation underscores the pivotal role of statistical learning in advancing safety within cyber physical transportation systems. By harnessing the power of data-driven insights, predictive modeling, and advanced analytics, this research contributes to the development of smarter, safer, and more resilient transportation systems. Chapter 2 proposes a novel stochastic jump-based model to capture the driving dynamics of safety-critical events. The identification of such events is challenging due to their complex nature and the high frequency kinematic data generated by modern data acquisition systems. This chapter addresses these challenges by developing a model that effectively represents the stochastic nature of driving behaviors and assume the happening of a jump process will lead to safety-critical situations. To tackle the issue of rarity in crash data, Chapter 3 introduces a variational inference of extremes approach based on a generalized additive neural network. This method leverages statistical learning to infer the distribution of extreme events, allowing for better generalization ability to unseen data despite the limited availability of crash events. By focusing on extreme value theory, this chapter enhances statistical learning's ability to predict and understand rare but high-impact events. Chapter 4 shifts focus to the safety validation of cyber-physical transportation systems, requiring a unique approach due to their advanced and complex nature. This chapter proposes a kernel-based method that simultaneously satisfies representativeness and criticality for safety verification. This method ensures that the safety evaluation process covers a wide range of scenarios while focusing on those most likely to lead to critical outcomes. In Chapter 5, a deep generative model is proposed to identify the boundary of safety-critical events. This model uses the encoder component to reduce high-dimensional input data into lower-dimensional latent representations, which are then utilized to generate new driving scenarios and predict their associated risks. The decoder component reconstructs the original high-dimensional case parameters, allowing for a comprehensive understanding of the factors contributing to safety-critical events. The chapter also introduces an adversarial perturbation approach to efficiently determine the boundary of risk, significantly reducing computational time while maintaining precision. Overall, this dissertation demonstrates the potential of using advanced statistical learning methods to enhance the safety and reliability of cyber-physical transportation systems. By developing innovative models and methodologies, this dissertation provides valuable tools and theoretical foundations for risk prediction, safety validation, and proactive management of transportation systems in an increasingly digital and interconnected world. / Doctor of Philosophy / Transportation is the foundation for modern society, cyber-physical systems are reshaping the future for automotive industry, holding a huge potential to make the transportation much safer and more efficient. Cyber-physical transportation systems are still in the phase of rapid development, ensuring the safety and reliability of these systems is crucial for its wide application. However, how to ensure safety for cyber-Physical Transportation System is still an open challenge. Statistical learning techniques offer a powerful way to analyze transportation data, providing insights that enhance safety. By leveraging data-driven insights, predictive modeling, and advanced analytics, this dissertation contributes to developing smarter, safer, and more resilient transportation systems. For better describing and identifying safety critical events, this dissertation propose a novel stochastic jump-based model helping to capture the dynamics of safety-critical events, a Variational Inference of Extremes approach to tackles the issue of limited crash data. Beside safety evaluation, a notable challenge for ensuring the safety of cyber-physical transportation system goes to how to test and develop robust control systems. To this end, Chapter 4 focuses on the safety validation of automated vehicles, proposing a kernel-based method that ensures both representativeness and criticality in safety verification. This approach covers a wide range of scenarios while concentrating on those most likely to lead to critical outcomes. Following the sampled cases, Chapter 5 proposes a data driven approach to identify the operational boundaries of safety-critical events. Overall, this dissertation demonstrates the potential of statistical learning to enhance transportation safety and reliability.

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O avanço recente da tecnologia na área de sistemas digitais representa uma grande oportunidade para realizar um importante progresso em diversos aspectos dos sistemas de controle e proteção tradicionais. No entanto, os requisitos provenientes do uso intensivo de software em sistemas críticos de segurança, aumenta a demanda por uma abordagem adequada que possa ser baseada na experiência nesta área. Apesar de vários modelos de capacidade de maturidade estarem em constante desenvolvimento, ainda é um desafio estabelecer uma forma coerente para a melhoria e avaliação do processo de desenvolvimento de software. O objetivo desta pesquisa é propor um método para obtenção de perfis de capacidade baseados na aplicação do modelo de referência brasileiro para melhoria do processo de software MR-MPS, em conjunto com a extensão de segurança do modelo de capacidade e maturidade CMMI-DEV +SAFE, embasado pela percepção de especialistas em segurança por meio da aplicação de um modelo de decisão por múltiplos critérios. / The recent technology advance in the digital systems area represents a great opportunity to make important progress in many aspects of traditional control and protection systems. However, requirements derived from the intensive use of software in safety critical systems raises the demand for a suitable approach that can be based on the expertise in this area. Although a number of capability maturity models have been in constant development, it is still challenging to establish a coherent path for software process improvement and evaluation. The goal of this research work is to propose a method for building capability profiles based on the application of the Brazilian Reference Model for Software Process Improvement MR-MPS, along with the Capability Maturity Model for Development safety extension CMMI-DEV +SAFE, supported by safety engineers insight through the application of a multi criteria decision model.