Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 119-125).

Prepared for the Office of Naval Research on ONR Contract no. N00014-77-C-0224. Issued by: M.I.T. Electronic Systems Laboratory, 1977; M.I.T. Laboratory for Information and Decision Systems, 1978-

Prepared for the Office of Naval Research on ONR Contract no. N00014-77-C-0224. Issued by: M.I.T. Electronic Systems Laboratory, 1977; M.I.T. Laboratory for Information and Decision Systems, 1978-

M.Ing. / Failure is a part of every day life. Information systems project failures are reported regularly in the media. The growth of the information technology industry in its entirety has been very dramatic over the last decade. Large information systems projects have always posed as a challenge for development teams and organizations. The nature of these challenges have increased with the growth of information systems projects over the last decade. With the critical role that information systems have in organizations, it is critical that information systems are delivered on time within budget and according to the requirements. Unfortunately many of the information systems projects that have been started, are cancelled, not on time or over budget. It is the purpose of this dissertation to discuss some of the managerial causes of why information systems projects fail, and also to address issues that could prevent the failure of information systems projects.

There is a world-wide drive to optimize maintenance decisions in an increasingly competitive manufacturing industry. Preventive maintenance if often the most organized and cost efficient strategy to follow, but a decision still has to be made on the optimal instant to perform preventive maintenance. Use based preventive maintenance decisions have been optimized through statistical analysis of failure date while predictive preventive maintenance (condition monitoring) has been optimized by utilizing more sophisticated technology. Very little work has however been done to combine the advantages of the two schools of thought. This thesis originated from a realization of the potential improvement in maintenance practice by combining use based preventive maintenance optimization techniques with high technology condition monitoring. In this thesis an approach is developed to estimate residual life of industrial equipment dynamically by combining statistical failure analysis and sophisticated condition monitoring technology. The approach is based on failure intensity proportions determined from historic survival time information and corresponding diagnostic information such as condition monitoring. Combined Proportional Intensity Models (PIMs) for non-repairable and repairable systems, containing the majority of conventional PIM enhancements as special cases, with numerical optimization techniques to solve for the regression coefficients, are derived. In addition to the residual life estimation approach, a user-friendly graphical method with which residual life estimates can be presented was also developed. This method is natural and easy to comprehend, even by inexperienced data analysts. The residual life estimation approach is applied to a typical data set from a South African industry and results are compared to those obtained from a similar, established maintenance decision support tool. This comparison showed that the approach developed in this thesis is relevant, practical and marginally better than the established decision support tool for certain criteria. / Thesis (PhD (Industrial Engineering))--University of Pretoria, 2006. / Industrial and Systems Engineering / unrestricted

Orientador: Robson Pederiva / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica / Made available in DSpace on 2018-07-24T02:53:53Z (GMT). No. of bitstreams: 1 Melo_GilbertoPechotode_D.pdf: 10688875 bytes, checksum: fb744df0fd371e40e7782c8c85921d94 (MD5) Previous issue date: 1998 / Resumo: Um dos fatores do grande interesse no desenvolvimento de novas técnicas de detecção de falhas é devido ao aumento da demanda da indústria em relação a segurança de seus sistemas, sendo eles supervisionados e monitorados para que as falhas sejam sanadas o mais rápido possível e que os distúrbios em operação normal não causem uma deterioração da performance dos mesmos. Neste trabalho, desenvolveu-se uma metodologia para detecção e localização de falhas em sistemas mecânicos utilizando observadores de estado de ordem reduzida. O método pode reconstruir os estados não medidos ou os valores provenientes de pontos de difícil acesso no sistema. Os parâmetros de interesse sujeitos a falhas são escolhidos, projetando-se um observador global otimizado para análise de todo o sistema considerando possíveis perturbações aleatórias na excitação, na resposta e falhas nos sensores. Projeta-se também observadores robustos a estes parâmetros de interesse, que localizam possíveis falhas ou irregularidades no sistema. Para os componentes que necessitem de um acompanhamento periódico devido às suas grandes solicitações ou falhas constantes, montam-se observadores com um sistema de alarmes que gera uma curva de tendências em um sistema automático para detecção e localização de falhas desenvolvido neste trabalho. Para os sistemas simulados e experimental, fez-se análises da performance transiente e em regime permanente, excitando-se os sistemas com força impulsiva, força senoidal, ruído aleatório, combinações dos mesmos, etc. Os resultados foram bons quando comparados com outros métodos e pôde-se também verificar os resultados através de uma bancada de testes / Abstract: The development of new faults detection techniques is necessary because of increasing demands from industries on reliability and safety in mechanical systems. They must be supervised such that occurrence of failures can be accommodated as quickly as possible because they can cause an unacceptable deterioration of the systems performance. In this work, we have developed a methodology to Detect and Isolate Faults in mechanical systems using reduced order state observers. We can monitor unmeasureble variables and the method selects the parameters from components that may fault during the process and constmcts an otimized global observer to analyze alI the system considering random noises in the excitation, in the response and sensor faults. To isolate component failures via robust observation, an automatic system with a bank of detection observers is constmcted, where each observer is only sensitive to one specified component failure while robust to alI other component failures. We have analized the transient and steady-state performance by exciting the system with impulsive force, sinosoidal force, random noise etc. The results were good when we compare them with other methods and we have verified the results through a testing rig / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica

For a long time mathematicians have been developing a number of theorems that seek to establish general structural and behavioral characteristics of dynamic systems. Most of these techniques based on calculus have been designed for the study of continuous phenomena. Hence they are ineffective to deals with discontinuous or divergent behaviors. Catastrophe theory, when applied to scientific problems, deal with the properties of discontinuities directly without reference to any specific underlying mechanism. It is especially suited to the study of systems in which the only reliable observations are of the discontinuities. System dynamics, introduced by professor Jay W. Forrester in the early 1960's, is used to represent general, complex dynamic systems. It focuses on the structure and behavior of systems composed of interacting feedback loops. The nature of its approach to modeling shares many common points with catastrophe theory. Particularly, both are used to seek to develop fruitful simplifications of a complex reality. The purposes of this thesis, therefore, are: first, to offer a qualitative as well as a quantitative description of catastrophe theory, as this theory is not very familiar to many people; secondly, to present the relationship between catastrophe theory and system dynamics; and thirdly, to apply these theorem to urban transportation planning. / Master of Science

Typescript (photocopy). / Digitized by Kansas Correctional Industries

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. Johannesburg, December 2014. / Industrial units cannot operate without failure forever. When the operation of a unit deviates from industrial standards, it is considered to have failed. The time from the moment a unit enters service until it fails is its lifetime. Within reliability and often in life data analysis in general, lifetime is the event of interest. For highly reliable units, accelerated life testing is required to obtain lifetime data quickly. Accelerated tests where failure is not instantaneous, but the end point of an underlying degradation process are considered. Failure during testing occurs when the performance of the unit falls to some specified threshold value such that the unit fails to meet industrial specifications though it has some residual functionality (degraded failure) or decreases to a critical failure level so that the unit cannot perform its function to any degree (critical failure). This problem formulation satisfies the random signs property, a notable competing risks formulation originally developed in maintenance studies but extended to accelerated testing here. Since degraded and critical failures are linked through the degradation process, the open problem of modelling dependent competing risks is discussed. A copula model is assumed and expert opinion is used to estimate the copula. Observed occurrences of degraded and critical failure times are interpreted as times when the degradation process first crosses failure thresholds and are therefore postulated to be distributed as inverse Gaussian. Based on the estimated copula, a use-level unit lifetime distribution is extrapolated from test data. Reliability metrics from the extrapolated use-level unit lifetime distribution are found to differ slightly with respect to different degrees of stochastic dependence between the risks. Consequently, a degree of dependence between the risks that is believed to be realistic to admit is considered an important factor when estimating the use-level unit lifetime distribution from test data. Keywords: Lifetime; Accelerated testing; Competing risks; Copula; First passage time.

A methodology for representing and analyzing manufacturing systems in a hybrid systems framework for control reconfiguration purposes in the presence of defects and failures at the product and system levels is presented. At the top level, a supervisory Petri net directs parts/jobs through the manufacturing system. An object-based hybrid systems model that incorporates both Petri nets at the event-driven level and differential equations at the time-driven level describes the subsystems. Rerouting capabilities utilizing this model at the product and operation levels were explained. Simulations were performed on a testbed model for optimal time and mode transition cost to determine the route for parts. The product level reconfiguration architecture utilizes an adaptive network-based fuzzy inference system (ANFIS) to map histogram comparison metrics to set-point adjustments when product defects were detected. Tests were performed on good and defective plastic parts from a plastic injection molding machine. In addition, a mode identification architecture was described that incorporates both time- and event-driven information to determine the operating mode of a system from measured sensor signals. Simulated data representing the measured process signals from a Navy ship chiller system were used to verify that the appropriate operating modes were detected.