A Low Frequency Uni-variate Model for the Effective Diagnosis and Prognosis of Bearing Signals Based Upon High Frequency Data

Abstract

Prognosis of rotating machinery is of vital importance to ensure ever increasing demands of availability, reduced maintenance expenditure and increased useful life are met. However, the prognosis of bearings typically employs techniques in the frequency or time-frequency domain due to the high frequency nature of the data involved (typically>20 KHz). This data quickly becomes unmanageable in practice and often has inferior prognostic horizons in comparison to those techniques which are based upon low frequency data analysis. This paper presents a novel methodology based upon the computation of the deviation from the empirically derived cumulative density function (CDF) of bearing data. For this purpose, the non-parametric, two sample, uni-variate Kolmogorov-Smirnov test is employed for the analysis. In particular, this paper focuses on mitigating the requirement of a-priori knowledge for bearing prognosis.

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Document Details

Document Type
Technical Report
Publication Date
Oct 02, 2014
Accession Number
AD1002239

Entities

People

  • Jamie L. Godwin
  • Peter Matthews

Organizations

  • Durham University

Tags

Communities of Interest

  • Biomedical
  • Engineered Resilient Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computational Science
  • Computer Science
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Failure Mode And Effect Analysis
  • Frequency
  • Frequency Domain
  • Information Science
  • Maintenance
  • Network Science
  • Normal Distribution
  • Signal Processing
  • Statistical Analysis

Fields of Study

  • Engineering

Readers

  • Atmospheric Science / Meteorology, specifically Wind Wave Turbulence.
  • Statistical inference.
  • Systems Analysis and Design