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A Generic Software Architecture for Prognostics (GSAP).

Christopher Teubert1, Matthew J Daigle1, Shankar Sankararaman2

  • 1NASA Ames Research Center, CA, 94035, USA.

International Journal of Prognostics and Health Management
|March 10, 2020
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Summary
This summary is machine-generated.

This paper introduces the Generic Software Architecture for Prognostics (GSAP), an open-source framework designed to simplify the development and deployment of prognostics applications. GSAP aims to accelerate the adoption of prognostics technology in various industries.

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Area of Science:

  • Systems Engineering
  • Reliability Engineering
  • Software Engineering

Background:

  • Prognostics, a systems engineering discipline for predicting component and system end-of-life, faces slow adoption due to development complexities.
  • A lack of open-source software frameworks hinders the practical application and integration of prognostics models and algorithms.
  • Existing challenges include perceived high hurdles in developing models, algorithms, architecture, and integration for prognostics.

Purpose of the Study:

  • Introduce the Generic Software Architecture for Prognostics (GSAP), an open-source software framework.
  • Reduce the effort and investment required for developing, testing, and deploying prognostics applications.
  • Enhance accessibility and accelerate the adoption of prognostics technology in industry.

Main Methods:

  • Designed GSAP as an open-source, cross-platform, object-oriented software framework and support library.
  • Described the requirements, design, and testing methodologies employed for GSAP.
  • Conducted a detailed case study focusing on battery prognostics to demonstrate GSAP's utility.

Main Results:

  • GSAP provides a comprehensive solution for creating prognostics applications.
  • The framework simplifies the development lifecycle, making prognostics more accessible.
  • A case study successfully demonstrated the application of GSAP in battery prognostics.

Conclusions:

  • GSAP effectively lowers the barrier to entry for prognostics development and implementation.
  • The open-source nature of GSAP promotes collaboration and faster innovation in the field.
  • GSAP is poised to significantly impact the practical application of prognostics across industries.