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.