Cognitive Technology for Advanced Maintenance Navy SBIR FY2006.2 Sol No.: Navy SBIR FY2006.2 Topic No.: N06-108 Topic Title: Cognitive Technology for Advanced Maintenance Proposal No.: N062-108-0637 Firm: Sentient Corporation 850 Energy Drive Idaho Falls, Idaho 83401 Contact: Sean Marble Phone: (802) 876-3100 Web Site: www.sentientscience.com Abstract: Although prognostic health monitoring systems for the mechanical aspects of the LAV-25 are currently under development, a comparable system for diagnosis of the electrical and weapons systems does not exist. System diagnosis is an expertise-based craft that is easily lost when turnover rates (due to promotion or personnel leaving the Corps) are rapid. Built in test (BIT) and built in test equipment (BITE) provide symptoms to assist the diagnosis, but they do not fully confirm where suspected problems reside. The complexity of indications, the potential number of indicators, and the many to one ratio of faults to indicators overwhelm maintainers trying to diagnose system faults. Factors such as usage, environment, and recent maintenance affect the way symptoms present. Maintainers require intelligent diagnostic systems that can recommend paths forward when maintainers are faced with complex symptoms. Sentient Corporation will leverage its expertise in system prognostics and automated reasoners to develop an intuitive Automated Intelligent Maintainer Support (AIMS) system with a light-weight, portable advanced user interface. AIMS will combine information derived from TETS with maintenance data, expert knowledge, BIT data, and FMECA, and will act as an assistant in the diagnosis and repair of problems in the on-board weapons systems of the LAV-25. AIMS will be adjustable for user experience level, self-learning to rapidly and fully leverage accumulated experience across the fleet, and flexible enough to be adapted to other weapons platforms in the future. Benefits: This project will significantly reduce the amount of time require for diagnosis of electronics faults in the LAV-25 weapon systems as well as reducing the burden of training on maintainers. Incorrect diagnosis of electronics faults leads to rework and time and equipment loss. In addition, junior maintainers are slower and more error prone than the experts that they replace. The Automated Intelligent Maintainer System will significantly reduce rework and serve as an aid to training, bringing new maintainers up to full capability than is currently possible. Through self-learning and networked interaction with other AIMS systems, each unit will benefit from fleet-wide experience, ensuring that new information on diagnosis of rare or novel faults is quickly disseminated. Return