The Navys ability to collect, store, and curate raw maintenance and inventory data continues to exceed its ability to effectively process it. Given the Navy's stakeholder-identified challenge to improve condition-based maintenance (CBM) with machinery monitoring and prognostics to maximize endurance and operational availability, and therefore readiness of Navy systems, Colvin Run Networks Inc. (Colvin Run) proposes a study of Navy-oriented machine-learning-enabled Predictive Maintenance And Inventory Management (PMIM) enhancement solution for CBM, SHIPMATE: Secure Hyper Intelligent Predictive Maintenance Analytics with Tactical Enhancements.
Benefit: Key benefits include improved mission readiness and sustainment program budgetary flexibility derived from return on investment in CBM algorithms for maritime platforms. Commercial applications include fleet management for airlines, cargo providers, and maintenance applications for other industrial systems.
Keywords: CBM, CBM, Machine Learning, Data Analytics, Condition Based Maintenance
