SBIR/STTR Award attributes
As next generation Automated Test Equipment (ATE) are produced and are integrated into current Automated Logistics Environments (ALE), a wealth of data is becoming available that can enhance current maintenance, inspection, and repair cycles. In addition, the advent of Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) frameworks have allowed maintainers to reduce costs, reduce risks, and improve operational lifetimes of deployed assets. This ability to anticipate failures before they occur require real-time production, ingestion, and interpretation of data streams. To bring this type of CBM and PHM capability into current Navy test processes, Mimyr is proposing an Automated Maintenance Prediction Service (AMPS). This solution amplifies and enhances existing test environments by providing a SaaS machine learning based prediction model that can both standardize and normalize any incoming data. This allows AMPS to be compatible with any legacy, current, and future test systems, creating a flexible, low-cost, adaptive, and rapidly deployable solution that can be used by any military or civilian group to improve and add capabilities to their existing workflows.
