Sustained Maintenance Planning Software

Total ownership costs and the life-cycle of mechanical components and systems can be vastly improved by increasing the reliability and overall operational readiness of the Navy fleet through the utilization of cost effective software techniques to utilize adaptive learning techniques to automatically understand component interdependencies and accurately predict component failure of equipment systems based on all available parametric data. Progeny believes integration of open-source software solutions with existing computer-controlled test and monitor systems that provide system status to predict failure will significantly decrease equipment maintenance costs by automatically recommending corrective hardware maintenance prior to failure occurring.Progenys proposed solution will leverage automated data acquisition, Natural Language Processing (NLP), big data storage techniques, and automatic calculation system-subsystem fatigue status and life remaining to recommend corrective maintenance.Our true innovation for this effort will be to provide a capability which automatically receives component fatigue damage assessment, calculates impacts to dependent hardware, and recommends condition-based maintenance. The resulting software will support unsupervised receipt and capturing of monitored damage assessment reports through integration of our solution with existing monitoring sensor hardware and reporting systems.