The Generic Software Architecture for Prognostics (GSAP), developed by members of the Diagnostics and Prognostics group, has been released under a NASA Open Source Agreement. GSAP can be found on the NASA Github site.
GSAP is a generic, extendable, flexible, modular C++ framework for applying prognostics technologies. GSAP manages top-level control, communications, logging, configuration, integration, and other general activities. A simple, standard interface is provided for integrating prognostics algorithms and models, minimizing the work required to deploy prognostics technologies. There is an included support library with tools to support prognostics, such as statistical tools and sampling functions for efficient uncertainty management, model support tools, and Bayesian filters, such as particle filtering and Kalman filtering. This library can be leveraged by users to create new algorithms and models. The standard interface allows for algorithms and models developed for GSAP to be reused anywhere GSAP is used.
The GSAP framework was released with a suite of example prognostics models and algorithms. Users can choose from these or develop their own models for their applications. GSAP facilitates the rapid development and deployment of prognostics solutions.
BACKGROUND: Prognostics is an emerging systems engineering discipline focused on predicting end-of-life of components and systems. The GSAP framework builds upon the algorithms, models, and integration expertise of the Diagnostics and Prognostics group. Lessons learned from past projects aided in producing a framework for applying prognostics and a library of support tools common for prognostics applications.
GSAP is meant to provide a mechanism for practitioners to easily integrate their prognostics needs on any platform, thereby lowering the integration hurdle that software products often face. As an example application, it has been integrated on a beaglebone single-board computer to provide prognostic information on remaining battery charge and remaining flight time for an electric Unmanned Aerial Vehicle (UAV).
NASA PROGRAM FUNDING: System-wide Safety Assurance Technologies (SSAT) project, Aviation Safety Program (AvSP), Aeronautics Research Mission Directorate (ARMD); Advanced Ground Systems Maintenance (AGSM) project, Ground Systems Development and Operations (GSDO) program, Human Exploration and Operations Mission Directorate (HEOMD); Automated Propellant Loading (APL) project, Advanced Exploration Systems (AES) program, HEOMD
TEAM: Matthew Daigle, Kai Goebel, Shankar Sankararaman (SGT), Chris Teubert, and Jason Watkins (California Space Grant)
POINT OF CONTACT: Chris Teubert, firstname.lastname@example.org