Distributed Condition Prognostics System for Navy Shipboard Machinery
Abstract
The University of South Carolina (USC), in collaboration with Georgia Institute of Technology (Georgia Tech), is proposing a computationally efficient distributed shipboard condition prognostics system. The system integrates sensing, feature extraction, nondestructive evaluation (NDE), structural health monitoring (SHM), low-computation Lebesgue sampling-based diagnostic and prognostic (LS-FDP) algorithms, uncertainty management, and probabilistic hierarchical reasoning. The proposed effort aims to provide effective assessment of the condition of shipboard rotating machinery systems and lower the operation and maintenance (O&M) cost. The proposed works will be tested on data of various fault modes, models with multiple interactive faults, and experimental testbed as a whole system. The proposed condition prognostics system is scalable, generic, easy-to-implement, and mathematically rigorous, which can be applied to a variety of Navy applications.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 09, 2018
- Source ID
- N001741710006
Entities
People
- Bin Zhang
Organizations
- United States Navy
- University of South Carolina