SBIR-STTR Award

Develop innovative methods and associated tools to support the definition of Automatic Test Equipment (ATE) capabilities and their individual instruments using an Open Architecture (OA) approach, to allow for precise understanding of the ATE, interoperability across Navy and DoD electronics maintenance, and improved utilization of Commercial Off-The-Shelf (COTS) tools to support ATE operations. These tools will be useful as an untapped resource to aid in ATS planning and support. Data mining can serve to expose run-time inefficiencies or under- and over-utilized test equipment (or specific capability ranges within a piece of equipment) and provide significant benefit to the selection of new ATE components during replacements and upgrades. Broad questions can be answered about ATE component capabilities including not only the frequency of their use but also the manner of their use. Additionally, such an analysis could identify economic targets of opportunity for the deployment of new and innovative test techniques.

Benefit:
Benefits of the proposed tools include gaining new capability to generate, monitor and understand ATE resource allocation, usage and instrument selection and setup. The specific applications chosen have wide DoD and commercial applications that will result in effective and efficient ATE designs, resource utilization and improve efficiency. Also, the revolutionary technologies contained in the proposal can be applied to other ATE applications and support equipment.

Keywords:
Diagnostics, Diagnostics, Signal Model, Instrument Capability Description, Open Architecture, Electronics Maintenance, Automatic Test Equipment

Develop innovative methods and associated tools to support the definition of Automatic Test Equipment (ATE) capabilities and their individual instruments using an Open Architecture (OA) approach, to allow for precise understanding of the ATE, interoperability across Navy and DoD electronics maintenance, and improved utilization of Commercial Off-The-Shelf (COTS) tools to support ATE operations. These tools will be useful as an untapped resource to aid in ATS planning and support. Data mining can serve to expose run-time inefficiencies or under- and over-utilized test equipment (or specific capability ranges within a piece of equipment) and provide significant benefit to the selection of new ATE components during replacements and upgrades. Broad questions can be answered about ATE component capabilities including not only the frequency of their use but also the manner of their use. Additionally, such an analysis could identify economic targets of opportunity for the deployment of new and innovative test techniques.

Benefit:
Benefits of the proposed tools include gaining new capability to generate, monitor and understand ATE resource allocation, usage and instrument selection and setup. The specific applications chosen have wide DoD and commercial applications that will result in effective and efficient ATE designs, resource utilization and improve efficiency. Also, the revolutionary technologies contained in the proposal can be applied to other ATE applications and support equipment. There is a great potential for the direct and widespread application of this technology both commercially and within the Government for test and repair applications. The tools could be integrated into a variety of test platforms and systems for terrestrial, aerial, surface and underwater vessels due to its flexible and open design. It will be a resource analysis tool. Examples for integration of the tool include all test systems in all services and industry. Thus, there is a huge potential for revolutionary improvement in maintenance capabilities and weapon system readiness by investigating ATE, TPS, and ITA resource selection, settings and usage. This proposal supports some Key Factors from OSD and Navy and other DoD services new directive as follows: 50% readiness not acceptable Readiness and Lethality are prime importance Turn data into something meaningful Smart data decisions data gathering Open Architecture Joint AI Intelligence Center Predictive maintenance No-Fault-Found (NFF) reliability maintenance 50% NFF Probably 75% out the door with Intermittent faults Joint intermittent test team Condition based maintenance plus (CBM Plus) Prognostic: collect data, move data, store, analyze, back to users All new weapon systems will use CBM Plus; rapidly accelerate CBM Plus Maintenance based on evidence: not only fix but prognosticate to increase MTBF Cost: maintenance test and repair - ATE replacement Cost driver 35% O&M Expand data across the enterprise Develop new capability for ATE Maintenance availability warehouse contains 1.5 billion records back to 2008

Keywords:
Standards 1641 1671, Signal Model, Automatic Test Equipment, Electronics Maintenance, Open Architecture, Instrument Capability Description, Diagnostics