Equipment behavior is often discussed in terms of age and use. For example, an automobile is frequently referred to 3 years old with 30,000 miles. Bivariate failure modeling provides a framework for studying system behavior as a function of two variables. This is meaningful when studying the reliability/availability of systems and equipment. This thesis extends work done in the area of bivariate failure modeling. Four bivariate failure models are selected for analysis. The study includes exploration of bivariate random number generation. The random data is utilized in estimating the bivariate renewal function and bivariate availability function. The two measures provide insight on system behavior characterized by multiple variables. A method for generating bivariate failure and repair data is developed for each model. Of the four models, two represent correlated random variables; the other two, stochastic functionally dependent variables. Also, methods of estimating the bivariate renewals function and bivariate availability function are constructed. The bivariate failure and repair data from the four failure models is incorporated into the estimation processes to study various failure scenarios. / Master of Science

Same types of systems with the same age usually have different amounts of cumulated usage. These systems when in operation usually have different performance and effectiveness. In this case the existing models of the univariate measures of system effectiveness are inadequate and incomplete. For example, the univariate availability measures for these same-aged systems are all the same even though with different amounts of usage. This is the motivation for this research to pursue a bivariate approach in reliability and maintenance modeling. This research presents a framework for bivariate modeling of a single-unit system. Five key efforts are identified and organized as: (i) bivariate failure modeling, (ii) bivariate renewal modeling, (iii) bivariate corrective maintenance (CM) modeling, (iv) bivariate preventive maintenance (PM) modeling, and (v) bivariate availability modeling. The results provide a foundation for further study of bivariate and multivariate models. For bivariate failure modeling, several bivariate failure models are constructed to represent the possible correlation structures of the two system aging variables, time and usage. The behavior of these models is examined under various correlation structures. The developed models are used to analyze example maintenance problems. Models for bivariate renewal, bivariate CM, and bivariate PM are derived based on the constructed bivariate failure models and the developed bivariate renewal theory. For bivariate CM modeling, corrective maintenance is modeled as an alternating bivariate renewal process or simply an ordinary bivariate renewal process. For bivariate PM modeling, PM models are examined under a bivariate age replacement preventive maintenance policy. The Laplace transforms of the renewal functions (and densities) for these models are obtained. Definitions for bivariate availability functions are developed. Based on the derived CM and PM models, the Laplace transforms for their corresponding bivariate availability models are constructed. The idea of the quality of availability measure is also defined in terms of bivariate availability models. The most significant observation is that this framework provides a new way to study the reliability and maintenance of equipment for which univariate measures are incomplete. Therefore, a new area of reliability research is identified. The definitions offered may be modified and the approach to model formulation presented may be used to define other models. / Ph. D.