Reliability of Systems Using Event Occurrence Networks

The study of a system's reliability has played a crucial role in business and industry since the dawn of modern technology. Current graphical models utilized in reliability theory are limited in that no one model or technique allows for a thorough analysis of system reliability. This research introduces a new graphical model and methodology to be used in the field of reliability that addresses this concern. Event Occurrence Networks (EONs) and their solution methodologies provide an all-inclusive graphical model that allows for the manipulation of several important reliability measures. An EON is a probabilistic network that represents the superposition of several terminating counting processes and is an efficient tool in both non-repairable and repairable systems. Current methodologies are also restricted in the distributions that characterize component life and repair times. This concern is alleviated via EONs coupled with piecewise polynomial approximation.

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.

This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.

As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.