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Model-Based Off-Nominal State Isolation and Detection System for Autonomous Fault Management
Completed
TRL 6 (started at 4, targeting 6)
Description
The proposed model-based Fault Management system addresses the need for cost-effective solutions that enable higher levels of onboard spacecraft autonomy to reliably maintain operational capabilities. The system will provide onboard off-nominal state detection and isolation capabilities that are key components to assessing spacecraft state awareness. The ability to autonomously isolate spacecraft failures to component levels will enable faster recovery thereby reducing down time. Model-based systems can provide better fault coverage than traditional limit-checking systems. The proposed system in particular will result in a relatively compact software package because it relies only on modeling nominal behavior; fault models are not needed. Thus this approach has the capability to detect any off-nominal behavior including un-modeled faults. Health information produced by the FM system can be used to make resource allocation and planning and scheduling decisions by ground operations or by other on-board autonomy agents. The system can be built and tested standalone potentially reducing FM developmental and testing costs. The FM system provides an evolutionary approach to full onboard autonomy as it can first be implemented and tested in ground-based systems and then migrated onboard spacecraft. Onboard fault management will be crucial to NASA mission success particularly during critical times where the situation changes rapidly and unpredictably with no opportunity for operator support.
Benefits
The need for the proposed capabilities is emerging as NASA seeks to provide higher quality fault management systems for its missions. The model-based fault detection and isolation system could support current and future programs with applications on the ground, in support of recovery operations, and in space, providing onboard autonomous fault detection and isolation. The fault management core diagnostic algorithms are general in nature and do not need tailoring to specific programs. User-supplied models allow customization to a particular target. Thus the developed system will be applicable to a very broad range of NASA mission classes from small to large, near-Earth to interplanetary, risk-adverse, and experimental. In particular missions such as Europa and Mars 2020 would greatly benefit from this technology. Ultimately, NASA and industry partner fault management products will enjoy a larger customer base. The potential market includes a wide range of customers from systems engineering, mission planning, and operations groups in all NASA centers especially ARC, JPL, and MSFC.
The need for robust and reliable onboard fault management will increase dramatically as spacecraft systems become more autonomous. The DoD's drive to increase situational awareness has already pushed it into exploration of autonomy-enabling architectures, including improved fault detection and isolation techniques, which will only increase as spacecraft autonomy moves into the broader spacecraft industry. The solution consists of diagnostic algorithms that utilize models provided by the users. The system can thereby be targeted for virtually any mission class. The proposed model-based FM solution is particularly well suited for spacecraft with modular HW/SW architectures. These new architectures will require updated approaches to FM and tools to support them. Potential non-NASA customers include: Large, medium and small prime contractors DoD Labs (NRL, AFRL) FFRDCs/UARCs (Aerospace Corporation, JHU/APL) Non-US organizations (ESA, JAXA, CNES, DLR). This technology is particularly suited for modular architectures such as the Space Missile Command's Modular Space Vehicle (MSV).
Details
| Technology area | Autonomous Systems > Reasoning and Acting Technologies > Fault Diagnosis and Prognosis |
| Program | Small Business Innovation Research/Small Business Tech Transfer (SBIR/STTR) |
| Lead organization | Okean Solutions, Inc., Seattle, WA |
| Start date | 2016-05-26 |
| End date | 2019-10-25 |
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