Fault Diagnosis and Prognosis Based on Lebesgue Sampling

Abstract

Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e. samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is especially true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation resources. This paper introduces the concept of Lebesgue sampling in FDP and proposes a Lebesgue sampling based fault diagnosis and prognosis (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of execution only when necessary is developed in computation cost reduction and uncertainty management. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines prognostic horizon on the state axis.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002748

Entities

People

  • Bin Zhang
  • Xiaofeng Wang

Organizations

  • University of South Carolina

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Complex Systems
  • Computations
  • Control Systems
  • Differential Equations
  • Electrical Engineering
  • Embedded Systems
  • Filtration
  • Kalman Filters
  • Particles
  • Probability
  • Reliability
  • Sampling
  • Sequential Monte Carlo Methods
  • Time Intervals

Fields of Study

  • Computer science
  • Engineering

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