Title: Machinery Life Cycle Cost Reduction Using CBM+ Predictive Maintenance Strategies
Author: Ruben Ortiz
Abstract: Innovative technological advances in weapons systems such as the Electromagnetic Railgun have created research challenges in advanced sophisticated war fighting that will drive the need to investigate predictive maintenance enablers such as diagnostics and prognostics while preserving its operational capabilities at low cost. Such endeavors greatly mitigate concerns regarding the rapid rise in degradation and catastrophic failure of machinery aboard naval vessels. In recent times failure events have placed the spotlight on the unintentional consequences of inadequate maintenance practices that no longer fully support product life cycle sustainment.
The subsequent impact to the operation and support logistics footprint are attributed to budgets and outdated maintenance strategies that do not take advantage of the current and future processing capability of large amounts of data and micro sized sensors in Navy systems. In this paper we examine the nature of an electrically powered rotating machine to demonstrate how CBM+ predictive maintenance strategies within the Prognostics Health Management framework address these maintenance gaps and allow for the planning of maintenance activities based on evidence of need. This predictive maintenance paradigm is the cornerstone of the modern high tech effort to reduce risk of machinery failures, increase operational availability and reduce life cycle costs.