Preventing bad things from happening to engineered systems, demands improvements to how we model their operation with regard to safety. Safety-critical and fiscally-critical systems both demand automated and exhaustive verification, which is only possible if the models of these systems, along with the number of scenarios spawned from these models, are tractably finite. To this end, this dissertation ad dresses problems of a model's tractability and usefulness. It addresses the state space minimization problem by initially considering tradeoffs between state space size and level of detail or fidelity. It then considers the problem of human interpretation in model capture from system artifacts, by seeking to automate model capture. It introduces human control over level of detail and hence state space size during model capture. Rendering that model in a manner that can guide human decision making is also addressed, as is an automated assessment of system timeliness. Finally, it addresses state compression and abstraction using logical fault models like fault trees, which enable exhaustive verification of larger systems by subsequent use of transition fault models like Petri nets, timed automata, and process algebraic expressions. To illustrate these ideas, this dissertation considers two very different applications - web service compositions and submerged ocean machinery. / by John C. Sloan. / Thesis (Ph.D.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Abstract not available

Monitoring, diagnosis and prediction of failures play key roles in automatic supervision of machine tools. They have received much attention because of the potential for reduced maintenance expenses, down time, and an increase in the equipment utilization level. At present, signal analysis techniques are predominantly used. But methods involving system analysis are capable of providing more reliable information, especially for predictive applications of supervision. System analysis involves comprehensive analytical models combined with techniques developed in control theory, and experimental modal analysis. The primary objective of this research is to develop a methodology to monitor critical physical parameters of mechanical systems, which are difficult to measure directly. These parameters are inherent features of constitutive rigid body models. A method for computer aided model generation developed in this thesis leads to a gray box model structure by which physical parameters can be estimated from experimental data. Lagrange's energy formalism, linear algebra and homogenous transformations are used to promote parsimonious three-dimensional model building. A software environment allowing symbolic and arbitrary precision computations facilitates efficient mapping of physical properties of the actual system into specific quantities of the analytical model. Six different methods are postulated and analyzed in this thesis to estimate physical parameters such as masses, stiffnesses and damping coefficients. Implementation of this methodology is a prerequisite for the design of an on-line monitoring and diagnosis system, which can detect and predict process faults. Two mechanical systems are used to validate the proposed methods: (1) A simple multi degree-of-freedom (MDOF) system and (2) a machine tool spindle assembly. A practical application of physical parameter estimation is proposed for preload monitoring in high-speed spindles. Preload variations in the bearing can lead to thermal instability and bearing seizure. The feasibility of using accelerometers located on the spindle housing to estimate bearing preload is evaluated. The optimal environment for continuation of this research is collaboration with machine tool companies to incorporate the proposed methodology (or parts of it) into current design practices. / Graduation date: 1999

Consideration of diagnosability in product design promises to increase product quality by reducing maintenance time without increasing cost or decreasing reliability. Methods for investigating the diagnosability of mechanical and electro-mechanical systems are described and are applied to the Bleed Air Control System (BACS) on the Boeing 747-400. The BACS is described and a diagnostic model is developed using information from the system Failure Modes and Effects Analysis. Emphasis is placed on the relationships between the system's functions and its components. Two metrics for the evaluation of system diagnosability and two metrics for the evaluation of component diagnosability are defined. These metrics emphasize diagnostic ambiguity and are combined with the probability of different system failures to weight the effects of each failure. Three modified systems are produced by reassigning functions from one component to another. The resulting effects on the system and component diagnosability are evaluated. We show that by changing these relationships system diagnosability can be improved without adding sensors or other components. / Graduation date: 1994

University of Macau / Faculty of Science and Technology / Department of Electromechanical Engineering

Optimum Sensor Localization/Selection in A Diagnostic/Prognostic Architecture Guangfan Zhang 107 Pages Directed by Dr. George J. Vachtsevanos This research addresses the problem of sensor localization/selection for fault diagnostic purposes in Prognostics and Health Management (PHM)/Condition-Based Maintenance (CBM) systems. The performance of PHM/CBM systems relies not only on the diagnostic/prognostic algorithms used, but also on the types, location, and number of sensors selected. Most of the research reported in the area of sensor localization/selection for fault diagnosis focuses on qualitative analysis and lacks a uniform figure of merit. Moreover, sensor localization/selection is mainly studied as an open-loop problem without considering the performance feedback from the on-line diagnostic/prognostic system. In this research, a novel approach for sensor localization/selection is proposed in an integrated diagnostic/prognostic architecture to achieve maximum diagnostic performance. First, a fault detectability metric is defined quantitatively. A novel graph-based approach, the Quantified-Directed Model, is called upon to model fault propagation in complex systems and an appropriate figure-of-merit is defined to maximize fault detectability and minimize the required number of sensors while achieving optimum performance. Secondly, the proposed sensor localization/selection strategy is integrated into a diagnostic/prognostic system architecture while exhibiting attributes of flexibility and scalability. Moreover, the performance is validated and verified in the integrated diagnostic/prognostic architecture, and the performance of the integrated diagnostic/prognostic architecture acts as useful feedback for further optimizing the sensors considered. The approach is tested and validated through a five-tank simulation system. This research has led to the following major contributions: ??generalized methodology for sensor localization/selection for fault diagnostic purposes. ??quantitative definition of fault detection ability of a sensor, a novel Quantified-Directed Model (QDG) method for fault propagation modeling purposes, and a generalized figure of merit to maximize fault detectability and minimize the required number of sensors while achieving optimum diagnostic performance at the system level. ??novel, integrated architecture for a diagnostic/prognostic system. ??lidation of the proposed sensor localization/selection approach in the integrated diagnostic/prognostic architecture.

In this dissertation, we investigate the theory and application of higher-order spectral analysis techniques to condition monitoring in shipboard electrical power systems. Monitoring and early detection of faults in rotating machines, such as induction motors, are essential for both preventive maintenance and to avoid potentially severe damage. As machines degrade, they often tend to become more nonlinear. This increased nonlinearity results in the introduction of new frequencies which satisfy particular frequency selection rules; the exact selection rule depends on the order of the nonlinearity. In addition, the phases of the newly generated frequencies satisfy a similar phase selection rule. This results in a phase coherence, or phase coupling, between the “original” interacting frequencies and the “new” frequencies. This phase coupling is a true signature of nonlinearity. Since the classical auto-power spectrum contains no phase information, the phase coupling signature associated with nonlinear interactions is not available. However, various higher-order spectra (HOS) are capable of detecting such nonlinear-induced phase coupling. The efficacy of the various proposed HOS-based methodologies is investigated using real-world vibration time-series data from a faulted induction motor driving a dc generator. The fault is controlled by varying a resistor placed in one phase of the three-phase line to the induction motor. First, we propose a novel method using a bispectral change detection (BCD) for condition monitoring. Even though the bicoherence is dominant and powerful in the detection of phase coupling of nonlinearly interacting frequencies, it has some difficulties in its application to machine condition monitoring. Basically, the bicoherence may not be able to distinguish between intrinsic nonlinearities associated with healthy machines and fault-induced nonlinearities. Therefore, the ability to discriminate the fault-only nonlinearities from the intrinsic nonlinearities is very important. The proposed BCD method can suppress the intrinsic nonlinearities of a healthy machine by nulling them out and thereby identify the fault-only nonlinearities. In addition, most machines contain rotating components, and the vibration fields they generate are periodic. These periodic impulse train signals may produce artificially high bicoherence values and can lead to ambiguous indications of faults in machine condition monitoring. The proposed BCD method can remove the artificially high bicoherence values caused by periodic impulse-train signals. With these advantages, the proposed BCD method is a new and sensitive indicator for condition monitoring. Second, we propose a novel method to estimate, from a measured single time-series data record, complex coupling coefficients in order to quantify the “strength” of nonlinear frequency interactions associated with rotating machine degradation. The estimation of the coupling coefficients is based on key concepts from higher-order spectral analysis and least mean-square-error analysis. The estimated coupling coefficients embody the physics of the nonlinear interactions associated with machine degradation and provide a quantitative measure of the “strength” of the nonlinear interactions. In addition, as an auto-quantity method utilizing a single time-series data record, the proposed method adds supplemental fault signature information to conventional tools. Such knowledge has the potential to advance the state-of-the-art of machine condition monitoring. Third, we propose a bispectral power transfer analysis methodology to quantify power transfer between nonlinearly interacting frequency modes associated with machine degradation. Our proposed method enables us to identify the relative amounts of power transferred by various nonlinear interactions, and thereby identify the predominant interactions. Such knowledge provides important new signature, or feature, information for machine condition monitoring diagnostics. / text

This thesis is the summation of two publications with the motivation to move reliability analysis earlier in the design process. Current analyses aim to improve reliability after components have been selected. Moving specific analyses earlier in the design process reduces the cost to the designer. These early design analyses provide information to the designer so that critical design changes can be made to avoid failures. The first presents failure rates for function-flow pairs. These function-flow failure rates are used in the Early Design Reliability Method (EDRM) to calculate system level reliability during functional design. This methodology is compared to the traditional reliability block diagram for three examples to show its usefulness during early conceptual design. Next, an extension to the Function Failure Design Method (FFDM) is presented. A more robust knowledge base using Failure Mode/Mechanism Distributions 1997 (FMD-97) has been implemented. Then failure rates from Nonelectric Parts Reliability Data (NPRD-95) are added to more effectively determine the likelihood that a failure mode will occur. The proposed Functional Failure Rate Design Method (FFRDM) uses functional inputs to offer recommendations to mitigate failure modes that have a high likelihood of occurrence. This work uses a past example where FFDM and Failure Modes and Effects Analysis (FMEA) are compared to show that improvements have been made. A four step process is presented to show how the FFRDM is used during conceptual design. / Graduation date: 2012

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2004. / Ajay Misra, Committee Member ; James Craig, Committee Member ; Richard Neu, Committee Member ; Daniel Schrage, Committee Member ; Dimitri Mavris, Committee Chair. Vita. Includes bibliographical references.

Thesis (Ph.D.)(Industrial and Systems Engineering))--University of Pretoria, 2006. / Includes summary. Includes bibliographical references. Available on the Internet via the World Wide Web.