Mechanical Engineering

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Advanced methodologies for reliability-based design optimization and structural health prognostics
(2010) Wang, Pingfeng; Youn, Byeng Dong; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Failures of engineered systems can lead to significant economic and societal losses. To minimize the losses, reliability must be ensured throughout the system's lifecycle in the presence of manufacturing variability and uncertain operational conditions. Many reliability-based design optimization (RBDO) techniques have been developed to ensure high reliability of engineered system design under manufacturing variability. Schedule-based maintenance, although expensive, has been a popular method to maintain highly reliable engineered systems under uncertain operational conditions. However, so far there is no cost-effective and systematic approach to ensure high reliability of engineered systems throughout their lifecycles while accounting for both the manufacturing variability and uncertain operational conditions. Inspired by an intrinsic ability of systems in ecology, economics, and other fields that is able to proactively adjust their functioning to avoid potential system failures, this dissertation attempts to adaptively manage engineered system reliability during its lifecycle by advancing two essential and co-related research areas: system RBDO and prognostics and health management (PHM). System RBDO ensures high reliability of an engineered system in the early design stage, whereas capitalizing on PHM technology enables the system to proactively avoid failures in its operation stage. Extensive literature reviews in these areas have identified four key research issues: (1) how system failure modes and their interactions can be analyzed in a statistical sense; (2) how limited data for input manufacturing variability can be used for RBDO; (3) how sensor networks can be designed to effectively monitor system health degradation under highly uncertain operational conditions; and (4) how accurate and timely remaining useful lives of systems can be predicted under highly uncertain operational conditions. To properly address these key research issues, this dissertation lays out four research thrusts in the following chapters: Chapter 3 - Complementary Intersection Method for System Reliability Analysis, Chapter 4 - Bayesian Approach to RBDO, Chapter 5 - Sensing Function Design for Structural Health Prognostics, and Chapter 6 - A Generic Framework for Structural Health Prognostics. Multiple engineering case studies are presented to demonstrate the feasibility and effectiveness of the proposed RBDO and PHM techniques for ensuring and improving the reliability of engineered systems within their lifecycles.
Critical Asset and Portfolio Risk Analysis for Homeland Security
(2008-07-21) McGill, William L; Ayyub, Bilal M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Providing a defensible basis for allocating resources for critical infrastructure and key resource protection is an important and challenging problem. Investments can be made in countermeasures that improve the security and hardness of a potential target exposed to a security hazard, deterrence measures to decrease the likeliness of a security event, and capabilities to mitigate human, economic, and other types of losses following an incident. Multiple threat types must be considered, spanning everything from natural hazards, industrial accidents, and human-caused security threats. In addition, investment decisions can be made at multiple levels of abstraction and leadership, from tactical decisions for real-time protection of assets to operational and strategic decisions affecting individual assets and assets comprising a regions or sector. The objective of this research is to develop a probabilistic risk analysis methodology for critical asset protection, called Critical Asset and Portfolio Risk Analysis, or CAPRA, that supports operational and strategic resource allocation decisions at any level of leadership or system abstraction. The CAPRA methodology consists of six analysis phases: scenario identification, consequence and severity assessment, overall vulnerability assessment, threat probability assessment, actionable risk assessment, and benefit-cost analysis. The results from the first four phases of CAPRA combine in the fifth phase to produce actionable risk information that informs decision makers on where to focus attention for cost-effective risk reduction. If the risk is determined to be unacceptable and potentially mitigable, the sixth phase offers methods for conducting a probabilistic benefit-cost analysis of alternative risk mitigation strategies. Several case studies are provided to demonstrate the methodology, including an asset-level analysis that leverages systems reliability analysis techniques and a regional-level portfolio analysis that leverages techniques from approximate reasoning. The main achievements of this research are three-fold. First, this research develops methods for security risk analysis that specifically accommodates the dynamic behavior of intelligent adversaries, to include their tendency to shift attention toward attractive targets and to seek opportunities to exploit defender ignorance of plausible targets and attack modes to achieve surprise. Second, this research develops and employs an expanded definition of vulnerability that takes into account all system weaknesses from initiating event to consequence. That is, this research formally extends the meaning of vulnerability beyond security weaknesses to include target fragility, the intrinsic resistance to loss of the systems comprising the asset, and weaknesses in response and recovery capabilities. Third, this research demonstrates that useful actionable risk information can be produced even with limited information supporting precise estimates of model parameters.
Automatic Generation of Generalized Event Sequence Diagrams for Guiding Simulation Based Dynamic Probabilistic Risk Assessment of Complex Systems
(2007-11-27) Nejad-Hosseinian, Seyed Hamed; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Dynamic probabilistic risk assessment (DPRA) is a systematic and comprehensive methodology that has been used and refined over the past two decades to evaluate the risks associated with complex systems such as nuclear power plants, space missions, chemical plants, and military systems. A critical step in DPRA is generating risk scenarios which are used to enumerate and assess the probability of different outcomes. The classical approach to generating risk scenarios is not, however, sufficient to deal with the complexity of the above-mentioned systems. The primary contribution of this dissertation is in offering a new method for capturing different types of engineering knowledge and using them to automatically generate risk scenarios, presented in the form of generalized event sequence diagrams, for dynamic systems. This new method, as well as several important applications, is described in detail. The most important application is within a new framework for DPRA in which the risk simulation environment is guided to explore more interesting scenarios such as low-probability/high-consequence scenarios. Another application considered is the use of the method to enhance the process of risk-based design.
Hybrid Causal Logic Methodology for Risk Assessment
(2007-11-27) Wang, Chengdong; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Probabilistic Risk Assessment is being increasingly used in a number of industries such as nuclear, aerospace, chemical process, to name a few. Probabilistic Risk Assessment (PRA) characterizes risk in terms of three questions: (1) What can go wrong? (2) How likely is it? (3) What are the consequences? Probabilistic Risk Assessment studies answer these questions by systematically postulating and quantifying undesired scenarios in a highly integrated, top down fashion. The PRA process for technological systems typically includes the following steps: objective and scope definition, system familiarization, identification of initiating events, scenario modeling, quantification, uncertainty analysis, sensitivity analysis, importance ranking, and data analysis. Fault trees and event trees are widely used tools for risk scenario analysis in PRAs of technological systems. This methodology is most suitable for systems made of hardware components. A more comprehensive treatment of risks of technical systems needs to consider the entire environment within which such systems are designed and operated. This environment includes the physical environment, the socio-economic environment, and in some cases the regulatory and oversight environment. The technical system, supported by an organization of people in charge of its operation, is at the cross-section of these environments. In order to develop a more comprehensive risk model for these systems, an important step is to extend the modeling capabilities of the conventional Probabilistic Risk Assessment methodology to also include risks associated with human activities and organizational factors in addition to hardware and software failures and adverse conditions of the physical environment. The causal modeling should also extend to the influence of regulatory and oversight functions. This research offers such a methodology. It proposes a multi-layered modeling approach so that most the appropriate techniques are applied to different individual domains of the system. The approach is called the Hybrid Causal Logic (HCL) methodology. The main layers include: (a) A model to define safety/risk context. This is done using a technique known as event sequence diagram (ESD) method that helps define the kinds of accidents and incidents that can occur in relation to the system being considered; (b) A model that captures the behaviors of the physical system (hardware, software, and environmental factors) as possible causes or contributing factors to accidents and incidents delineated by the event sequence diagrams. This is done by common system modeling techniques such as fault tress (FT); and (c) A model to extend the causal chain of events to their potential human and organizational roots. This is done using Bayesian belief networks (BBN). Bayesian belief networks are particularly useful as they do not require complete knowledge of the relation between causes and effects. The integrated model is therefore a hybrid causal model with the corresponding sets of taxonomies and analytical and computational procedures. In this research, a methodology to combine fault trees, event trees or event sequence diagrams, and Bayesian belief networks has been introduced. Since such hybrid models involve significant interdependencies, the nature of such dependencies are first determined to pave the way for developing proper algorithmic solutions of the logic model. Major achievements of this work are: (1) development of the Hybrid Causal Logic model concept and quantification algorithms; (2) development and testing of computer implementation of algorithms (collaborative work); (3) development and implementation of algorithms for HCL-based importance measures, an uncertainty propagation method the BBN models, and algorithms for qualitative-quantitative Bayesian belief networks; and (4) development and testing of the Integrated Risk Information System (IRIS) software based on HCL methodology.
On the Theoretical Foundations and Principles of Organizational Safety Risk Analysis
(2007-08-02) Mohaghegh-Ahmadabadi, Zahra; Mosleh, Ali; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This research covers a targeted review of relevant theories and technical domains related to the incorporation of organizational factors into technological systems risk. In the absence of a comprehensive set of principles and modeling guidelines rooted in theory and empirical studies, all models look equally good, or equally poor, with very little basis to discriminate and build confidence. Therefore, this research focused on the possibility of improving the theoretical foundations and principles for the field of Organizational Safety Risk Analysis. Also, a process for adapting a hybrid modeling technique, in order to operationalize the theoretical organizational safety frameworks, is proposed. Candidate ingredients are techniques from Risk Assessment, Human Reliability, Social and Behavioral Science, Business Process Modeling, and Dynamic Modeling. Then, as a realization of aforementioned modeling principles, an organizational safety risk framework, named Socio-Technical Risk Analysis (SoTeRiA)is developed. The proposed framework considers the theoretical relation between organizational safety culture, organizational safety structure/practices, and organizational safety climate, with specific distinction between safety culture and safety climate. A systematic view of safety culture and safety climate fills an important gap in modeling complex system safety risk, and thus the proposed organizational safety risk theory describing the theoretical relation between two concepts to bridge this gap. In contrast to the current safety causal models which do not adequately consider the multilevel nature of the issue, the proposed multilevel causal model explicitly recognizes the relationships among constructs at multiple levels of analysis. Other contributions of this research are in implementing the proposed organizational safety framework in the aviation domain, particularly the airline maintenance system. The US Federal Aviation Administration (FAA), which has sponsored this research over the past three years, has recognized the issue of organizational factors as one of the most critical questions in the quest to achieve 80% reduction in aviation accidents. An example of the proposed hybrid modeling environment including an integration of System Dynamics (SD), Bayesian Belief Network (BBN), Event Sequence Diagram (ESD), and Fault Tree (FT), is also applied in order to demonstrate the value of hybrid frameworks. This hybrid technique integrates deterministic and probabilistic modeling perspectives, and provides a flexible risk management tool.
INTEGRATING SOFTWARE BEHAVIOR INTO DYNAMIC PROBABILISTIC RISK ASSESSMENT
(2005-12-21) Zhu, Dongfeng; Smidts, Carol; Mosleh, Ali; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
Software plays an increasingly important role in modern safety-critical systems. Although research has been done to integrate software into the classical Probability Risk Assessment (PRA) framework, current PRA practice overwhelmingly neglects the contribution of software to system risk. The objective of this research is to develop a methodology to integrate software contributions in the Dynamic Probabilistic Risk Assessment (DPRA) environment. DPRA is considered to be the next generation of PRA techniques. It is a set of methods and techniques in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. DPRA allows consideration of dynamic interactions of system elements and physical variables. The fact remains, however, that modeling software for use in the DPRA framework is also quite complex and very little has been done to address the question directly and comprehensively. This dissertation describes a framework and a set of techniques to extend the DPRA approach to allow consideration of the software contributions on system risk. The framework includes a software representation, an approach to incorporate the software representation into the DPRA environment SimPRA, and an experimental demonstration of the methodology. This dissertation also proposes a framework to simulate the multi-level objects in the simulation based DPRA environment. This is a new methodology to address the state explosion problem. The results indicate that the DPRA simulation performance is improved using the new approach. The entire methodology is implemented in the SimPRA software. An easy to use tool is developed to help the analyst to develop the software model. This study is the first systematic effort to integrate software risk contributions into the dynamic PRA environment.
Access Scheduling and Controller Design in Networked Control Systems
(2005-10-05) Zhang, Lei; Hristu-Varsakelis, Dimitrios; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
A Networked Control System (NCS) is a control system in which the sensors and actuators are connected to a feedback controller via a shared communication medium. In an NCS, the shared medium can only provide a limited number of simultaneous connections for the sensors and actuators to communicate with the controller. As a consequence, the design of an NCS involves not only the specification of a feedback controller but also that of a communication policy that schedules access to the shared communication medium. Up to now, this task has posed a significant challenge, due in large part to the modeling complexity of existing NCS architectures, under which the control and communication design problems are tightly intertwined. This thesis proposes an alternative NCS architecture, whereby the plant and controller choose to ``ignore'' the actuators and sensors that are not actively communicating. This new architecture leads to simpler NCS models in which the design of feedback controller and communication polices can be effectively decoupled. In that setting, we propose a set of medium access scheduling strategies and accompanying controller design methods that address a broad range of stabilization, estimation, and optimization problems for a general class of NCSs. The performance of the proposed methods is illustrated through a set of simulations and hardware experiments.

Mechanical Engineering

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