Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems

Abstract

We present a new procedure to generate additional features for system diagnosis. The procedure is based on empirical mode decomposition of measured signals obtained by monitoring the relevant state of a system. This procedure is different from the existing procedures for defining features, which are generally obtained using the statistics of the measured signal, the matched filter outputs, and the wavelet decomposition of measured signals. Features derived by this new procedure complement the existing features for diagnosis, and therefore they should improve performance of the classifier used to diagnose systems. We illustrate the procedure by generating new features for diagnosis of the AH64A helicopter transmission assembly.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2008
Accession Number
ADA487732

Entities

People

  • Hiralal Khatri
  • Kenneth Ranney
  • Kwok Tom
  • Romeo Del Rosario

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Amplitude
  • Data Fusion
  • Data Science
  • Data Sets
  • Decomposition
  • Detection
  • Detectors
  • Frequency
  • Helicopters
  • Information Science
  • Machine Learning
  • Measurement
  • Military Research
  • Monitoring
  • Power Levels
  • Signal Processing
  • Statistics

Readers

  • Artificial Intelligence
  • Computer Vision.
  • Image Processing and Computer Vision.