Methods to Improve the Prognostics of Time-to-Failure Models

Abstract

Autonomous and autonomic systems have started to develop machine learning (ML) methods for prognostics and health management (PHM) directly at the platform level. Remaining-useful-life (RUL) estimation, also known as Time-to-failure (TTF) estimation, using streaming sensor data is critical for PHM as it can help to decide and schedule appropriate courses of action (COAs). This work casts the RUL-estimation problem as a classification problem over a finite-time horizon. Rather than using a winner-take-all method to develop a RUL estimator, we propose a top-K estimator that considers the RUL values corresponding to the K-largest probabilities yielded by the classifier to develop our estimator. The top-K RUL values can be used to drive the execution of conservative or aggressive PHM strategies, or be tracked over time to develop robust RUL estimators that leverage the history of RUL estimates. The performance of the proposed RUL estimators is illustrated on a dataset from NASA's Prognostics Center of Excellence.

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Document Details

Document Type
Technical Report
Publication Date
Nov 24, 2021
Accession Number
AD1158568

Entities

People

  • Charles Hsu
  • Edward Baumann
  • Gray Selby
  • Pedro A. Forero

Organizations

  • Naval Information Warfare Center Pacific

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Big Data
  • Classification
  • Data Mining
  • Data Sets
  • Delphi Method
  • Dimensionality Reduction
  • Engineering
  • Estimators
  • Failure Mode And Effect Analysis
  • Information Science
  • Machine Learning
  • Neural Networks
  • Pattern Recognition
  • Probability
  • Random Variables
  • Reliability
  • Reliability Engineering
  • Supervised Machine Learning
  • Test And Evaluation

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Autonomy