LASERNET Optical Oil Debris Monitor

Abstract

The LASERNET optical oil debris monitor has been developed for real time online identification of fault type and severity through detection of size, shape and rate of production of failure related debris in critical applications such as engines and gearboxes of helicopters. Detection of failure related debris without sampling has required the development of a high resolution high speed imaging and processing capable of recording and analyzing images at rates up to 500 frames per second. We have designed and constructed such a system based on parallel/series CCD technology, high speed dedicated image processors and neural net classifiers. The system has been tested on the T700 engine at the helicopter power train test cell at Naval Air Warfare Center, Trenton, NJ. Qualitative performance evaluation has demonstrated the ability to detect debris in real time and to distinguish and classify air bubble patterns in varying degrees of complexity. The overall false alarm rate depends on the strategy adopted in the image processor. For a dual processor architecture the results indicate that LASERNET will be capable of operating with a false alarm (defined as an incorrect identification of a rejectable gear box) rate of less than one every 2000 operating hours.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA347454

Entities

People

  • A. Schultz
  • J. Reintjes
  • L. L. Tankersley
  • M. D. Duncan
  • T. L. Mcclelland

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aerial Warfare
  • Air Force
  • Cameras
  • Condition Based Maintenance
  • Detection
  • Detectors
  • False Alarms
  • Field Programmable Gate Arrays
  • High Resolution
  • High Speed Cameras
  • Identification
  • Image Processing
  • Laser Science
  • Military Research
  • Particles
  • Reliability
  • Warning Systems

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Regression Analysis.
  • Tribology (the study of the boundary interaction between sliding surfaces, lubrication, wear and friction).