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Diagnostics & Prognostics Group Releases Prognostic Models Python Packages v1.1
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Diagnostics & Prognostics Group Releases Prognostic Models Python Packages v1.1

Researchers from the Intelligent Systems Division's Diagnostics & Prognostics group and the Prognostics Center of Excellence (PCoE) have released the first revision of their Open-Source Prognostics Models Python Package: prog_models v1.1. Together with the Prognostics Algorithms Package, these releases provide modular, extendable tools for creating and simulating degradation (prognostics) models, performing systems health-state estimation and prediction, creating new systems health-state estimation and prediction algorithms, benchmarking prognostics performance, and visualizing results. These tools are intended to help researchers in industry, academia, and government build new models, algorithms, and software architectures for prognostics.

The Prognostics Models and Alogorithms Packages are being used by projects in the Aeronautics Research Mission Directorate (ARMD), Space Technology Mission Directorate (STMD), and Human Exploration Operations Mission Directorate (HEOMD) for the development of new models and algorithms for fault estimation, prognostics, and system-wide safety estimation. The new features and increased stability from this release will enable these projects to provide more capable and accurate models and algorithms. Specific work using this software includes:

  • ARMD Data and Reasoning Fabric (DRF) - providing prognostics services of the Python Prognostics as a Service (PaaS) software as the foundation for trajectory generation
  • ARMD System Wide Safety (SWS) Program - developing and testing new algorithms and models contributing to the calculation and prediction of safety metrics for the National Airspace (NAS)
  • STMD Distributed Spacecraft Autonomy (DSA) / HEOMD Autonomous Spacecraft Operations projects – used in testing and evaluating novel spacecraft load estimation and prognostics technology
  • STMD Early Career Initiative (ECI) SmartSTAMPS proposal (currently under consideration) – would be used in a novel, palm-sized, mobile wireless group of sensor nodes for anomaly detection and health monitoring
  • Python PaaS software - providing prognostics services to many agents in parallel through Service Oriented Architectures (SOAs) in various software systems and research applications

The packages will be updated with new features as work progresses. See the prog_models v1.1 Release Notes on GitHub for more details. Both the Prognostics and Algorithms Packages can be downloaded from GitHub as well.

BACKGROUND: The Diagnostics & Prognostics (D&P) group has a rich history of releasing software to the broader Prognostics Health Management (PHM) community. Past software releases include the Generic Software Architecture for Prognostics (GSAP), the MATLAB Python Libraries, the Hybrid Diagnostics Engine (HyDE), and XPlaneConnect. In recent years, the PHM community has been moving towards Python as the primary development language. As a result, last October, the D&P group started work on a set of Python prognostics research tools that eventually became the Python Prognostics Packages.

NASA PROGRAM FUNDING: Autonomous Systems and Operations (ASO) project (POC: Jeremy Frank), Human Exploration Operations Mission Directorate (HEOMD); and the System Wide Safety (SWS) project (POC: Wendy Okolo), Aeronautics Research Mission Directorate (ARMD)

TEAM: Matteo Corbetta, Chetan Kulkarni, Wade Lambertson (Intern), Chris Teubert, and Jason Watkins

POINT OF CONTACT: Chris Teubert,

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