This paper aims to develop an integrated shipboard condition prognostics system that integrates sensing, feature extraction, and particle filtering-based diagnostic and prognostic algorithms with applications to bearing systems. 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 work is 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.
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Health management; shipboard rotating machinery
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