Using knowledge-informed machine learning on the PRONOSTIA (FEMTO) and IMS bearing data sets. Predict remaining-useful-life (RUL).

The NASA Prognostic Model Package is a Python framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components.

The NASA Prognostic Python Packages is a Python framework focused on defining and building models and algorit for prognostics (computation of remaining useful life) of engineering systems, and provides a set of models and algorithms for select components developed within this framework, suitable for use in prognostic applications.

The Prognostic Algorithm Package is a python framework for model-based prognostics (computation of remaining useful life) of engineering systems, and provides a set of algorithms for state estimation and prediction, including uncertainty propagation. The algorithms take as inputs prognostic models (from NASA's Prognostics Model Package), and per…

Evolutionary Neural Architecture Search on Transformers for RUL Prediction

ML Approaches for RUL Prediction, Anomaly Detection, Survival Analysis and Failure Classification

remaining useful life, residual useful life, remaining life estimation, survival analysis, degradation models, run-to-failure models, condition-based maintenance, CBM, predictive maintenance, PdM, prognostics health management, PHM

The NASA Prognostics As-A-Service (PaaS) Sandbox is a simplified implementation of a Software Oriented Architecture (SOA) for performing prognostics (estimation of time until events and future system states) of engineering systems. The PaaS Sandbox is a wrapper around the Prognostics Algorithms Package and Prognostics Models Package, allowing on…

Tools to test BNN inference algorithms and techniques to predict RUL on aeronautical systems.

Machine Data Hub Web App

A curated collection of papers, articles and datasets to keep modern industrial engineer up-to-date on new strategies for anticipating failures using sensors historical data.

Machine learning algorithm to predict the long-term adverse cardiovascular events following coronary artery bypass surgery (CABG)

Implementation of UD_ARULE CLI version within Python to process feature data (FD) nodes of a system/system-of-systems to predict key prognostic parameters such as RUL, PH, and SoH