APPROXIMATE PREDICTION OF MULTIMODAL CREST STATISTICS AND SYSTEM RELIABILITY FOR IMPULSIVE NOISE LOADING

Abstract

The current analytical models of impulsive noise and system response thereto are found numerically intractable, indicating a need for engineering approximations. One, based on finding an 'equivalent' unimodal system having approximately the same response statistics--here the crest statistics--is examined. It is found that fair agreement obtains, making it possible to use two reasonably accessible parameters to characterize any linear multimodal system response and thus predict its crest statistics systematically, provided that 'standard' curves of unimodal response crest statistics are available for statistically identical forcing. Limitations of this promising approach are explored, encouraging its use in reliability prediction pending further studies. Also, the need for further studies of impulsive noise response crest distribution 'tails' is noted for prediction of overload and wearout reliability on the basis of wear-dependent failure rate concepts, since extrapolation of empirical data into the 'tail' (here done on the basis of a Weibull crest fit) is increasingly necessary the more 'impulsive' the response is (i.e., the more the parameter is less than unity).

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1968
Accession Number
AD0668767

Entities

People

  • George L. Hedin
  • R. F. Lambert
  • Raymond A. Janssen
  • T. I. Smits

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Agreements
  • Bandwidth
  • Complex Systems
  • Data Science
  • Electrical Engineering
  • Engineering
  • Failure Mode And Effect Analysis
  • Frequency Domain
  • Information Science
  • Materials
  • Random Variables
  • Reliability
  • Resonant Frequency
  • Standards
  • Statistical Analysis
  • Statistical Distributions
  • Statistics

Fields of Study

  • Engineering

Readers

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