Intelligent condition-based engine/equipment management system

Abstract

Health management of machines, such as gas turbine engines and industrial equipment, offers the potential benefits of efficient operations and reduced cost of ownership. Machine health management goes beyond monitoring operating conditions, it assimilates available information and makes the most favorable decisions to maximize the value of the machine. These decisions are usually related to predicted failure modes and their corresponding failure time, recommended corrective actions, repair/maintenance actions, and planning and scheduling options. Hence machine health management provides a number of functions that are interconnected and cooperative to form a comprehensive health management system. While these interconnected functions may have different names (or terminology) in different industries, an effective health management system should include four primary functions: sensory input processing, fault identification, failure/life prediction, planning and scheduling. These four functions form the foundation of the method of ICEMS (Intelligent Condition-based Engine/Equipment Management System). To facilitate information processing and decision making, these four functions may be repartitioned and regrouped, such as for network based computer software designed for health management of sophisticated machinery.