Evaluation of fatigue damage classification based on probabilistic weibull analysis

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Abstract

This paper focuses on the evaluation of fatigue damage classification based on statistical probability distribution using Weibull analysis. Feature extractions, i.e., kurtosis, wavelet-based energy, and fatigue damage, were calculated from the segments of fatigue strain signal. The feature extractions were then classified using artificial neural network (ANN) approach in order to find the class or level of fatigue damage. Subsequently, the statistical distribution fitting, i.e., Weibull, was applied to evaluate the fatigue damage classification. Based on the results, the accuracy of the ANN classification was found at 92% and a total of five classes or levels of fatigue damages were obtained. Based on Weibull distribution, when the maximum fatigue damage for the first class is inserted at approximately 8.42 × 10-5 around 69% of the coil spring will have a probability of failure. When the higher fatigue damage is inserted in the fifth class at 1.65 × 10-3 about 99% of the component will have probability to failure. The results show that fatigue damage classification is consistent with the Weibull distribution.

Original languageEnglish
Pages (from-to)189-195
Number of pages7
JournalIranian Journal of Science and Technology - Transactions of Mechanical Engineering
Volume41
Issue number3
DOIs
Publication statusPublished - 1 Sep 2017

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Fatigue damage
Weibull distribution
Feature extraction
Neural networks
Probability distributions
Fatigue of materials

Keywords

  • Classification
  • Fatigue damage
  • Feature extraction
  • Probability
  • Weibull distribution

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering

Cite this

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title = "Evaluation of fatigue damage classification based on probabilistic weibull analysis",
abstract = "This paper focuses on the evaluation of fatigue damage classification based on statistical probability distribution using Weibull analysis. Feature extractions, i.e., kurtosis, wavelet-based energy, and fatigue damage, were calculated from the segments of fatigue strain signal. The feature extractions were then classified using artificial neural network (ANN) approach in order to find the class or level of fatigue damage. Subsequently, the statistical distribution fitting, i.e., Weibull, was applied to evaluate the fatigue damage classification. Based on the results, the accuracy of the ANN classification was found at 92{\%} and a total of five classes or levels of fatigue damages were obtained. Based on Weibull distribution, when the maximum fatigue damage for the first class is inserted at approximately 8.42 × 10-5 around 69{\%} of the coil spring will have a probability of failure. When the higher fatigue damage is inserted in the fifth class at 1.65 × 10-3 about 99{\%} of the component will have probability to failure. The results show that fatigue damage classification is consistent with the Weibull distribution.",
keywords = "Classification, Fatigue damage, Feature extraction, Probability, Weibull distribution",
author = "Yunoh, {M. F.M.} and Shahrum Abdullah and {Md Saad}, {Mohamad Hanif} and {Mohd Nopiah}, Zulkifli and Nuawi, {Mohd. Zaki}",
year = "2017",
month = "9",
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doi = "10.1007/s40997-016-0058-9",
language = "English",
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AU - Nuawi, Mohd. Zaki

PY - 2017/9/1

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N2 - This paper focuses on the evaluation of fatigue damage classification based on statistical probability distribution using Weibull analysis. Feature extractions, i.e., kurtosis, wavelet-based energy, and fatigue damage, were calculated from the segments of fatigue strain signal. The feature extractions were then classified using artificial neural network (ANN) approach in order to find the class or level of fatigue damage. Subsequently, the statistical distribution fitting, i.e., Weibull, was applied to evaluate the fatigue damage classification. Based on the results, the accuracy of the ANN classification was found at 92% and a total of five classes or levels of fatigue damages were obtained. Based on Weibull distribution, when the maximum fatigue damage for the first class is inserted at approximately 8.42 × 10-5 around 69% of the coil spring will have a probability of failure. When the higher fatigue damage is inserted in the fifth class at 1.65 × 10-3 about 99% of the component will have probability to failure. The results show that fatigue damage classification is consistent with the Weibull distribution.

AB - This paper focuses on the evaluation of fatigue damage classification based on statistical probability distribution using Weibull analysis. Feature extractions, i.e., kurtosis, wavelet-based energy, and fatigue damage, were calculated from the segments of fatigue strain signal. The feature extractions were then classified using artificial neural network (ANN) approach in order to find the class or level of fatigue damage. Subsequently, the statistical distribution fitting, i.e., Weibull, was applied to evaluate the fatigue damage classification. Based on the results, the accuracy of the ANN classification was found at 92% and a total of five classes or levels of fatigue damages were obtained. Based on Weibull distribution, when the maximum fatigue damage for the first class is inserted at approximately 8.42 × 10-5 around 69% of the coil spring will have a probability of failure. When the higher fatigue damage is inserted in the fifth class at 1.65 × 10-3 about 99% of the component will have probability to failure. The results show that fatigue damage classification is consistent with the Weibull distribution.

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KW - Probability

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