SBIR-STTR Award

Prognostics and health management (PHM) systems are critical for detecting impending faults and enabling a proactive decision process for maintenance or replacement of avionics systems before actual failures occur. A PHM system is essential to enhancing aircraft systems reliability and maintaining a high level of mission readiness and affordability. Current PHM advancements are focused on aircraft structures and electro-mechanical components. There is a need to address the unique PHM system-level design characteristics for avionics systems. This effort investigates the development of a toolset to enable the integration of data, models and algorithms for system-level prognostics and health management of avionics systems. The effort researches and characterizes a systematic framework for the integration, processing, and distribution of health state data from onboard monitoring systems to off-board Automatic Test Systems (ATS). This includes investigating the application of the latest Condition Based Maintenance (OSA-CBM), MIMOSA, ISO and IEEE ATS-related standards to provide a standard, common model (structure) for exchange of health state data and information across the maintenance infrastructure. Including assessment of data analysis and modeling techniques to enable system-level health assessment and performance life remaining predictions. A technology development plan and a desktop proof-ofconcept demonstration for a small target system are part of this effort.

Benefit:
The technology developed under this effort can be easily applied in other domains such as commercial aviation, sea, space and ground vehicle platforms, in order to advance the implementation of Condition Based Maintenance (CBM+) principles. In addition, there is a big potential for commercialization. For example, the technology resulting from this effort can be applied in other industries including, commercial aviation, power utilities, automotive, consumer appliances, medical equipment, and any commercial plants where failures in large scale manufacturing systems have a great economic impact.

Keywords:
, , Health monitoring, Predictive maintenance, Condition Based Maintenance, Avionics, Modeling and Analysis, PHM, Electronics Prognostics

The scope of the Phase II efforts is to develop and demonstrate the underlying software technology and use case implementation process for a common Modeling Environment for Data-Driven Diagnostics and Prognostics Development. The work performed under this effort will advance the electronics prognostics and health management (PHM) toolset and development framework defined under the Phase I research, add components to support multiple aircraft domains, and integrate relevant tools and research work conducted by our research partner, Montana State University. Using the work products produced during the Phase I effort, the objective is to advance this work by developing a Minimum Viable Product (prototype software) and move toward a common, standards-based, modeling environment to support the development of data-driven diagnostics and prognostics applications for the aircraft automated ground support system. The modeling environment enables support of automatic test system (ATS) and PHM capability development and maturity level enhancement processes. The efforts are designed to advance the ePHM toolset and development framework defined by GSS under the Phase I efforts to enable implementations across aviation platforms. The value of this approach to the U.S. Navy (and the DoD) is that the modeling environment can support a legacy platform, and at the same time move toward a standardized open system to support new platforms. This dual path of maturation and transition process provides for immediate impact on legacy platforms and positioning for transition to support future fleet platforms.

Benefit:
The market potential for health monitoring systems and related technologies is very promising. The key is that the market for these technologies is just developing and the solutions are truly dual-use technologies that apply to both government and commercial market sectors. GSS observes that there are several efforts throughout the defense and commercial aviation industry focused on collecting data produced by the platforms, but no one is addressing the standards-based toolset delivered by the concept described in this proposal. Our business objective in pursuing the funding for this project is to bridge the technology gap which prevents DoD from building an organic prognostics application development capability and, at the same time, develop opportunities in applicable commercial sectors.

Keywords:
Diagnostics, Predictive maintenance, CBM, PHM, Prognostics, data-driven models, ATS, CBM+