Overview of Wavelet/Neural Network Fault Diagnostic Methods Applied to Rotating Machinery

Abstract

New technology in the form of wavelet-based methods coupled with intelligent classification schemes built around neural networks, can drive the development of substantially improved fault detection and identification (FDI) methods. Such systems represent important next generation FDI kernels for integration into advanced condition based maintenance systems for rotating machinery. This paper presents an overview of the results obtained by ALPHATECH in a program aimed at developing wavelet/neural network based FDI systems for vibrating machinery. The paper presents the performance results of these methods applied to a range of platforms including helicopter transmissions, turbopumps, and gas turbines. In addition, enhancements to the basic fault detection and identification system are presented and include overviews of multi-sensor wavelet-based differential features and improved FDI performance through classification fusing using hierarchical neural networks.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1996
Accession Number
ADP010198

Entities

People

  • Inna A. Yeldham
  • Jose E. Lopez
  • Kevin Oliver

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Detection
  • Detectors
  • False Alarms
  • Feature Extraction
  • Frequency
  • Gas Turbines
  • Identification
  • Identification Systems
  • Image Processing
  • Mechanical Phenomena
  • Mechanical Structure
  • Neural Networks
  • Production Engineering
  • Reliability
  • Time Intervals
  • Turbines
  • Wavelet Transforms

Fields of Study

  • Computer science
  • Engineering

Readers

  • Fault Tolerant Diagnosis of Black and White Balloon Isolation Tests Using ¥.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks