Feature Extraction for Bearing Prognostics and Health Management (PHM) - A Survey (Preprint)

Abstract

Feature extraction in bearing PHM involves extracting characteristic signatures from the original sensor measurements, which are sensitive to bearing conditions and thus most useful in determining bearing faults. The quality of extracted features directly affects the performance and the effectiveness of bearing PHM. Feature extraction is therefore a critical component in bearing PHM. To optimally improve PHM effectiveness and minimize maintenance costs of bearings, a large amount research has been conducted in extracting salient features for PHM, which leads to a considerable number of feature extraction techniques. Our main effort in this paper is to survey some major techniques explored so far, focusing on more recent advancements. Our endeavor also includes pointing out the advantages and disadvantages of each of those techniques. This paper attempts to serve as a general reference for bearing PHM practitioners and as a general guide for choosing proper feature extraction methods for bearing PHM systems.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2008
Accession Number
ADA490114

Entities

People

  • Hai Qiu
  • Naresh Iyer
  • Weizhong Yan

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Computational Science
  • Damage Detection
  • Data Mining
  • Digital Signal Processing
  • Failure Mode And Effect Analysis
  • Feature Extraction
  • Frequency Bands
  • Information Science
  • Machine Learning
  • Measurement
  • Neural Networks
  • Nonlinear Dynamics
  • Pattern Recognition
  • Signal Processing
  • Supervised Machine Learning

Fields of Study

  • Engineering

Readers

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Technology Areas

  • AI & ML