The research discusses interrelationship of structural and reliability importance measures which used in the probabilistic safety assessment. The most frequently used component importance measures, such as Birnbaum's Importance (BI), Risk Reduction (RR), Risk Reduction Worth (RRW), RA (Risk Achievement), Risk Achievement Worth (RAW), Fussel Vesely (FV) and Critically Importance (CI) can be derived from two structure importance measures that are developed based on the size and the number of Minimal Path Set (MPS) and Minimal Cut Set (MCS). In order to show an effectiveness of importance measures which is developed in this paper, the three representative functional structures, such as series-parallel, k out of n and bridge are used to compare with Birnbaum's Importance measure. In addition, the study presents the implementation examples of Total Productive Maintenance (TPM) metrics and alternating renewal process models with exponential distribution to calculate the availability and unavailability of component facility for improving system performances. System state structure functions in terms of component states can be converted into the system availability (unavailability) functions by substituting the component reliabilities (unavailabilities) for the component states. The applicable examples are presented in order to help the understanding of practitioners.

This paper proposes various measures of reliability, maintainability and availability in information technology service management(ISTM). Global ITSM standards such as ITIL(IT Infrastructure Library) and ISO/IEC 20000 are introduced. Especially availability management of De-facto and De-juire standards is concerned with users and customers of IT service. The study presents seven reliability measures, six maintainability measures and two availability measures. These results can be effectively used with a appropriate IT availability management tools such as CFIA(Component Failure Impact Analysis) and SOA(Service Outage Analysis), etc.

This paper presents three methods for expression of reliability measures for large and small data. First method is to express parametric estimation of cardinal reliability measure data for large sample, which requires numerous sample. Second is to obtain nonparametric distribution classification of ordinal reliability measure data for small sample. However it is difficult for field user to understand this method. Last method is to acquire parametric estimation of ordinal reliability measure data for small data. Because this method requires small sample and is comprehensive, we recommend this one among the proposed methods. Various reliability rank measures are presented.

This paper is to propose two computation procedures of reliability measures for large interval data. First method is efficient to verify the relationship among four reliability measures such as F(t), R(t), f(t) and λ(t). Another method is effective to interpret the concept of various reliability measures. This study is also to reinterpret and recompute the errors of four reliability measures discovered in the reliability textbooks. Various numerical examples are presented to illustrate the application of two proposed procedures.

This paper presents the practial guide to implementation of reliability distributions. The applicability and property of various reliability distribution will then be illustrated. Main objective of this study is to present how to use reliability distributions summary with respect to the total life cycle management. This paper provides insight info the good aspects of using relability distributions properly.