Sense and Respond Technology Enabling Condition Based Maintenance (CBM)
Navy SBIR FY2014.1

Sol No.: Navy SBIR FY2014.1
Topic No.: N141-030
Topic Title: Sense and Respond Technology Enabling Condition Based Maintenance (CBM)
Proposal No.: N141-030-0061
Firm: Real-Time Innovations
232 East Java Drive
Sunnyvale, California 94089
Contact: Kenneth Brophy
Phone: (408) 990-7400
Web Site:
Abstract: Condition-based Maintenance (CBM) of the Littoral Combat Ship (LCS) assets is preferred over the scheduled maintenance because CBM provides a window into the future of the asset's performance and enables service only when needed. The asset performance indicators captured on both LCS variants must be reduced, transmitted (off-ship), and mined using shore-based predictive analytics. Real-Time Innovations (RTI), Inc. is proposing an extensible data-centric bus architecture to integrate shipboard asset performance data with shore-based predictive analysis tools. To address the interoperability challenge, the solution uses automatic transformation of formally described data models using the Object Management Group (OMG) Model-Driven Architecture (MDA). The proposed approach is based on the OMG Data Distribution Service (DDS), which is widely deployed in the Navy including the LCS. The peer-to-peer bus architecture of DDS greatly simplifies plug-n-play application integration by using formally described, normalized data models. The end-to-end solution also includes network protocols equipped with compression algorithms suitable for low-bandwidth ship-to-shore communication. Furthermore, Navy's Information Assurance (IA) requirements are implemented using the OMG Secure-DDS standard. The proposed solution will improve LCS combat readiness using a truly interoperable data-bus for exchanging CBM data from the ship to shore while reducing distractions to the sailors, standby inventory requirements, and the decision time for the analysts.
Benefits: This technology will improve Littoral Combat Ship (LCS) mission readiness without increasing its manpower requirements. The technology will help reduce LCS maintenance costs by extending the life of the shipboard assets. When commercialized, this technology will help the growing data-center predictive maintenance industry to interoperate with the existing data-center infrastructure management tools. Industrial sectors that employ massive data-center, such as banking, finance, healthcare, transportation, tele-communication, factory automation, and many others will observe increased automation and reduced burden during asset maintenance.