Real-Time Condition Based Maintenance for High Value Systems
Abstract
Many industries operate high value equipment often remotely -- that requires reliable performance in severe environments. Similarly, the U.S. Navy's submarine p (TASs) stress conventional approaches to operating and maintaining this system level capability comprised of integrated hydraulic, mechanical, electronic and acoustic sub-systems. The Navy invested in a Condition Based Maintenance (CBM) proof of concept for an individual ship TAS by developing the Thinline Health Monitoring System (THMS). THMS collects real-time discrete reliability data and synchronizes this data with other historical information and the TAS's current condition assessment. As a predictive "intelligent code" it uses Bayesian Belief Networks (BBNs) to extract the full value of real-time data and provide a complete range of system performance evaluations -- from diagnosis to prediction. Drawing upon THMS' success, the U.S. Navy supported expanding this capability fleet- wide to encompass health assessments of the entire submarine TASs population. Plans have been developed to build a relational database that is accessible to a geographically separated towed systems community via the Internet for interactive analysis and diagnostics. These system level analyses and first principal processes are directly translatable to other government and commercial critical systems that cannot afford unscheduled -- or unnecessary -- maintenance.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 05, 2001
- Accession Number
- ADP013482
Entities
People
- William W. Matzelevich