Diagnostic Analyzer for Gearboxes (DAG): User's Guide. Version 3.1 for Microsoft Windows 3.1.

Abstract

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1997
Accession Number
ADA324929

Entities

People

  • Kourosh Danai
  • Vinay B. Jammu

Organizations

  • Glenn Research Center

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Abnormalities
  • Abstracts
  • Algorithms
  • Analyzers
  • Computers
  • Detection
  • Detectors
  • Directories
  • Engineering
  • Industrial Engineering
  • Machine Learning
  • Military Research
  • Modulus Of Elasticity
  • Neural Networks
  • Operating Systems
  • Test Sets
  • Vibration

Fields of Study

  • Computer science
  • Engineering

Readers

  • Artificial Intelligence
  • Computer Science.
  • Tribology (the study of the boundary interaction between sliding surfaces, lubrication, wear and friction).

Technology Areas

  • AI & ML