Mathematical Frameworks for Diagnostics, Prognostics and Condition Based Maintenance Problems

Abstract

This report documents the theoretical and computational investigation of statistical pattern recognition techniques and Bayesian Networks (BN). The application of statistical pattern recognition methodology and Bayesian Networks to automatic fault diagnostics, fault prognostics, and condition based maintenance (CBM) is explored. The theory of Margin-Setting, a new pattern recognition method developed by researchers at Alabama A&M University, is documented and its applicability to problems of interest to Army is investigated. Extensive parametric studies of Margin-Setting have been carried out through training and testing the algorithms with real and simulated data. Effects of various parameters including size of the training set, the evolution of prototypes, threshold level, margin value, etc. on the accuracy of the algorithm will be studied for various types of classification problems. The interplay between false positives and false negatives as they relate to system parameters and the application environment will be studied. Algorithms for mapping the data sets of large-scale Bayesian Network graph structures in a parallel and distributed computing environment were researched. In support of the Condition Based Maintenance (CBM) philosophy, a theoretical framework and algorithmic methodology for obtaining useful diagnostic and prognostic data from electro-mechanical systems was developed. The methods are based on vibration and modal analyses of the physical components. To illustrate the concept of the derived process, two "real world" models, a PCI circuit card, and an example rotor hub were considered. Models were created using finite element analysis (FEA) techniques, and analyzed to determine fundamental mode shapes and vibration frequencies. The results yielded vibration modes characteristic of both undamaged and damaged systems. An ?(N?logN) pattern recognition technique was utilized for signal discrimination.

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Document Details

Document Type
Technical Report
Publication Date
Aug 15, 2008
Accession Number
ADA500002

Entities

People

  • Andrew E Scott
  • Kaveh Heidary

Organizations

  • Alabama A & M College

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Computer Programming
  • Computers
  • Data Mining
  • Data Science
  • Databases
  • Failure Mode And Effect Analysis
  • Information Processing
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Relational Database Management Systems
  • Statistical Algorithms
  • Supervised Machine Learning
  • Two Dimensional

Readers

  • Aerospace Test and Evaluation
  • Logistics and Supply Chain Management.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference