K-means clustering analysis and artificial neural network classification of fatigue strain signals

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3 Citations (Scopus)

Abstract

This paper focuses on the analysis based on the clustering and the classification method of fatigue strain signals. Very few detailed studies have been carried out on the classification of fatigue damage, especially in the automotive field. Fatigue strain signals were observed on the coil springs of vehicles during road tests. The strain signals were then extracted using the Wavelet Transform approach. The features extraction was grouped using the K-means clustering method to obtain the appropriate number of data groups. A classification process was executed to obtain the optimum pattern recognition through the use of artificial neural network (ANN). Based on the results of the ANN classification with an accuracy of 92 %, a total of five classes or levels of fatigue damage were obtained. Based on the results, the data distribution was mostly scattered in the lower class, namely in the first class with the fatigue damage ranging from 1.98 × 10−7 to 8.18 × 10−5. The highest fatigue damage was in the fifth class with values ranging from 1.14 × 10−3 to 1.65 × 10−3. Based on this clustering and classification, the level of fatigue damage could be classified into five stages.

Original languageEnglish
Pages (from-to)757-764
Number of pages8
JournalJournal of the Brazilian Society of Mechanical Sciences and Engineering
Volume39
Issue number3
DOIs
Publication statusPublished - 1 Mar 2017

Fingerprint

Fatigue damage
Fatigue of materials
Neural networks
Wavelet transforms
Pattern recognition
Feature extraction

Keywords

  • Artificial neural network
  • Classification
  • Clustering
  • Fatigue damage
  • K-means clustering

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

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title = "K-means clustering analysis and artificial neural network classification of fatigue strain signals",
abstract = "This paper focuses on the analysis based on the clustering and the classification method of fatigue strain signals. Very few detailed studies have been carried out on the classification of fatigue damage, especially in the automotive field. Fatigue strain signals were observed on the coil springs of vehicles during road tests. The strain signals were then extracted using the Wavelet Transform approach. The features extraction was grouped using the K-means clustering method to obtain the appropriate number of data groups. A classification process was executed to obtain the optimum pattern recognition through the use of artificial neural network (ANN). Based on the results of the ANN classification with an accuracy of 92 {\%}, a total of five classes or levels of fatigue damage were obtained. Based on the results, the data distribution was mostly scattered in the lower class, namely in the first class with the fatigue damage ranging from 1.98 × 10−7 to 8.18 × 10−5. The highest fatigue damage was in the fifth class with values ranging from 1.14 × 10−3 to 1.65 × 10−3. Based on this clustering and classification, the level of fatigue damage could be classified into five stages.",
keywords = "Artificial neural network, Classification, Clustering, Fatigue damage, K-means clustering",
author = "Yunoh, {M. F M} and Shahrum Abdullah and {Md Saad}, {Mohamad Hanif} and {Mohd Nopiah}, Zulkifli and Nuawi, {Mohd. Zaki}",
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AB - This paper focuses on the analysis based on the clustering and the classification method of fatigue strain signals. Very few detailed studies have been carried out on the classification of fatigue damage, especially in the automotive field. Fatigue strain signals were observed on the coil springs of vehicles during road tests. The strain signals were then extracted using the Wavelet Transform approach. The features extraction was grouped using the K-means clustering method to obtain the appropriate number of data groups. A classification process was executed to obtain the optimum pattern recognition through the use of artificial neural network (ANN). Based on the results of the ANN classification with an accuracy of 92 %, a total of five classes or levels of fatigue damage were obtained. Based on the results, the data distribution was mostly scattered in the lower class, namely in the first class with the fatigue damage ranging from 1.98 × 10−7 to 8.18 × 10−5. The highest fatigue damage was in the fifth class with values ranging from 1.14 × 10−3 to 1.65 × 10−3. Based on this clustering and classification, the level of fatigue damage could be classified into five stages.

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KW - K-means clustering

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