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

Content Provider	Springer Nature Link
Author	Saad, M. H. M. Nopiah, Z. M. Nuawi, M. Z. Abdullah, S. Yunoh, M. F. M.
Copyright Year	2016
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.
Starting Page	757
Ending Page	764
Page Count	8
File Format	PDF
ISSN	16785878
Journal	Journal of the Brazilian Society of Mechanical Sciences and Engineering
Volume Number	39
Issue Number	3
e-ISSN	18063691
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-05-28
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification Artificial neural network Mechanical Engineering Clustering K-means clustering Fatigue damage
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Industrial and Manufacturing Engineering Automotive Engineering Mechanical Engineering Aerospace Engineering

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