Prognostic Health Management of DoD Assets

Abstract

This proposed research will synergistically developed numerical models, experiments, algorithms, and other tools applicable to prognostic health management. The topics covered included (1) Characterize acoustic emission signals from different failure modes in composite materials, and identification of the features of critical damage, (2) Experimental characterization of the influence of attenuation on acoustic emission signals in carbon epoxy composite laminates (3) Characterization of acoustic emission signals during fatigue crack growth in aluminum panels, (4) Characterization of fretting related acoustic emission signals, in which acoustic emission characteristics were correlated with the features of the surfaces participating in the frictional process, (5) Identification for the first time the horizontal shear component of acoustic emission signals and designing sensors capable of detecting these components, along with securing a US patent on this sensor, (6) Development of a Multiagent based structural health monitoring system, (7) Development of a machine learning system for classifying acoustic emission signals, and (8) Supporting summer internships for three minority high school students during the summer of 2014 and another three minority high school students which is planned during the summer of 2015.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2015
Accession Number
ADA624505

Entities

People

  • Albert Esterline
  • Mannur J. Sundaresan

Organizations

  • North Carolina Agricultural and Technical State University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Composite Materials
  • Computational Science
  • Dimensionality Reduction
  • Factor Analysis
  • Failure Mode And Effect Analysis
  • Information Science
  • Kernel Functions
  • Laminates
  • Machine Learning
  • Material Degradation Processes
  • Materials Laboratories
  • Materials Processing
  • Materials Testing
  • Mechanics
  • Network Science
  • Polymer Matrix Composites
  • Supervised Machine Learning

Fields of Study

  • Engineering

Readers

  • Military Leadership and Professional Education.
  • Sensor Fusion and Tracking Systems.
  • Structural Health Monitoring of Composite Structures.

Technology Areas

  • AI & ML