Browsing by Subject "Reliability (Engineering)"
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Item A flexible bathtub hazard model for nonrepairable systems(Texas Tech University, 1985-12) Jaisingh, Lloyd RNot availableItem A new block preventive maintenance policy for stochastically failing items(Texas Tech University, 1982-12) Lin, Lee-eng ShirleyNot availableItem On fault identification in certain diagnosable systems(Texas Tech University, 1983-08) Krothapalli, Virabrahma PrasadNot availableItem One-stage and two-stage machining economics problems with stochastic tool life and penalty cost for tool failure during production(Texas Tech University, 1985-12) Koulamas, Christos P.Item Optimal availability allocation in a multi-stage system(Texas Tech University, 1969-08) Wilkinson, Robert ENot availableItem Optimal maintenance schedules and reliability analysis of systems subject to stochastic failure(Texas Tech University, 1980-05) Sherif, Yosef ShukriNot availableItem Performance and tool reliability for metallic composites(Texas Tech University, 1991-05) Kim, Yon SooThis research defined, investigated, and developed a performance reliability concept, which uses on-line performance information and is capable of working in the computer aided manufacturing environment. Furthermore, the conditional performance reliability structure developed is capable of real-time, look-ahead projections of a failure-free "next" cycle. The conditional tool performance reliability structure enables one to maximize system through-put and product quality as well as resources. In the performance domain, physical performance is a measure that represents some degree of system, subsystem, component or device success in a continuous sense, as opposed to a classical binomial sense (success or failure). If applicable sensing and monitoring means exist, physical performance can be observed over time, along with explanatory variables or covariables. Performance reliability represents the probability that performance will remain satisfactory over a finite period of time or usage cycles in the future. An empirical physical performance function is constructed to incorporate the explanatory variables, operating, and environmental conditions over a time or usage dimension. This function enables one to model device performance and the associated classical reliability measures simultaneously, in the performance domain, when a performance critical limit (which represents an appropriate definition of failure in terms of performance) is set at a fixed level, based on application requirements. After the performance reliability theory was developed, it was demonstrated through a carbide-tipped HSS drilling tool example. This example was based on cutting forces (thrust) generated while drilling Duralcan aluminum composites. The development included the capability for online, real-time conditional tool reliability prediction as well as traditional classical reliability measures. In the case of inadequate knowledge of the failure mechanics, this empirical modeling concept along with performance degradation knowledge can serve as an important analysis tool in reliability work in product and process improvement. This research provides an innovative linkage between actuarial and physical based reliability work. Results are expressed in a non-parametric form, with minimal assumptions. The parametric case is demonstrated using the normal distribution to represent performance measurements. Traditional regression analysis and response surface techniques can be used to develop performance function models. The resulting failure density, cumulative failure density, reliability and failure hazard functions are empirical in nature and follow the time or usage dimension of the performance data.Item Reliability of a Gallium Arsenide MESFET(Texas Tech University, 1980-12) Yin, Chenwei JohnNot Available.Item Reliability prediction of the series system with spares subject to Weibull failure(Texas Tech University, 1984-12) Pan, Jeh-nanThis research effort has developed a mathematical model for predicting the system reliability of a series system with spares subject to Weibull failures. Many modern equipment systems are designed in a series configuration with spares available when necessary. The Weibull distribution is one of the most widely applied and flexible failure distributions used to describe and model present day single component systems. Due to the theoretical intractability of the Weibull distribution in spares models, it is extremely difficult to solve the multiple integration involved in this type of model analytically. Therefore, a numerical integration method using Simpson's Rule was selected as a tool to address the problem of multiple integration of the Weibull distribution. Then, a recursive algorithm was developed for the reliability prediction of a series system with spares subject to a general failure model (including the Weibull distribution). The developed model has been validated for accuracy using a two component series system with spares. A sensitivity analysis has also been performed on the two parameters of the Weibull distribution, and the effect of adding spares or stages on the system reliability has been assessed. This research also demonstrates the application of the developed reliability prediction model with a realistic example pertaining to a tool reliability problem common to manufacturing industries.Item Smooth Empirical Bayes Estimation of Observation Error Variances in Linear Systems(Texas Tech University, 1971-12) Lian, Mingwei GeorgeNot Available.Item Some practical empirical Bayes procedures for use in Weibull reliability estimation(Texas Tech University, 1970-12) Couture, Donald JosephNot availableItem System self-assessment of survival in time series modeling(Texas Tech University, 1998-05) Lu, HuitianThe concept, theoretical argument, and practical implementation of system self-assessment of survival using time series modeling is defined , investigated, and developed. System self-assessment of survival predicts conditional reliability for a future period of time or usage, to support an operational mission in real-time. As implemented, performance measures are monitored and modeled in physical terms, then associated models are developed in probability/statistical terms. The key issues in system self-assessment of survival are physical performance measurement and related modeling, forecasting, and survival estimation. The research develops theoretical connections between physical performance assessment and existing time series modeling, yielding a self-assessment of survival model, based on the concept of performance reliability. Different methods, including Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, and realtime recurrent neural networks, are assessed regarding modeling and prediction capabilities in real-time. In order to meet the real-time requirements of self-assessment of survival, model "self-generation" is emphasized in the context of on-line performance observation. For demonstration and validation, the research work develops the framework of a deliverable software package, Real-Time System Self-Assessment of Survival (RTSAS), which performs real-time data acquisition and survival selfassessment. The research describes methods useful for system self-assessment of survival based on physical system performance measures and time series modeling in both single failure mode and multiple, independent, failure modes. Results produced in linear trend exponential smoothing show promise for field real-time applications, provided resolution of physical signals can be obtained and the failure mode is properly defined in terms of physical performance.Item Weibull parameter estimation from noisy failure data(Texas Tech University, 1975-05) Ezzat, Mohamed OmarNot available