A review of localised time-frequency features classification associated to fatigue data analysis

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3 Citations (Scopus)

Abstract

The paper presents a review of the rational to perform the localised time-frequency fatigue damage feature classifications, which can be categorised as an alternative approach for fatigue life prediction that is relatively new in this research field. It is a good need to have a study in fatigue feature classification that lead to the formation of a new guideline and enable a design to the same reference level as well as high reliability towards the maximum usage. Consequently, this review paper emphasis on the concentration for performing the localised time-frequency feature classification approach as a scientific and engineering knowledge advancement in about fatigue of material and structures. Hence, related approaches to be said as the subject contents, i.e. fatigue life prediction models, signal processing approaches, the implementation of segmentation and clustering methods towards fatigue data, as well as data classification that lead for pattern recognition technique. It is known from the literature about the selection of the appropriate approaches which were often based on the analyst's experience and preferences. By predicting the structure fatigue life, which needs only several variable and will automatically calculate, classify and optimise the severity of fatigue damage through the significant mathematical and experimental findings, leading to cost and time saving.

Original languageEnglish
Pages (from-to)960-976
Number of pages17
JournalInternational Review of Mechanical Engineering
Volume7
Issue number5
Publication statusPublished - 2013

Fingerprint

Fatigue of materials
Fatigue damage
Knowledge engineering
Pattern recognition
Signal processing
Costs

Keywords

  • Clustering
  • Fatigue
  • Features classification
  • Review
  • Signal processing

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

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title = "A review of localised time-frequency features classification associated to fatigue data analysis",
abstract = "The paper presents a review of the rational to perform the localised time-frequency fatigue damage feature classifications, which can be categorised as an alternative approach for fatigue life prediction that is relatively new in this research field. It is a good need to have a study in fatigue feature classification that lead to the formation of a new guideline and enable a design to the same reference level as well as high reliability towards the maximum usage. Consequently, this review paper emphasis on the concentration for performing the localised time-frequency feature classification approach as a scientific and engineering knowledge advancement in about fatigue of material and structures. Hence, related approaches to be said as the subject contents, i.e. fatigue life prediction models, signal processing approaches, the implementation of segmentation and clustering methods towards fatigue data, as well as data classification that lead for pattern recognition technique. It is known from the literature about the selection of the appropriate approaches which were often based on the analyst's experience and preferences. By predicting the structure fatigue life, which needs only several variable and will automatically calculate, classify and optimise the severity of fatigue damage through the significant mathematical and experimental findings, leading to cost and time saving.",
keywords = "Clustering, Fatigue, Features classification, Review, Signal processing",
author = "Yunoh, {M. F M} and Shahrum Abdullah and {Mohd Nopiah}, Zulkifli and Nuawi, {Mohd. Zaki}",
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AU - Abdullah, Shahrum

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AU - Nuawi, Mohd. Zaki

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N2 - The paper presents a review of the rational to perform the localised time-frequency fatigue damage feature classifications, which can be categorised as an alternative approach for fatigue life prediction that is relatively new in this research field. It is a good need to have a study in fatigue feature classification that lead to the formation of a new guideline and enable a design to the same reference level as well as high reliability towards the maximum usage. Consequently, this review paper emphasis on the concentration for performing the localised time-frequency feature classification approach as a scientific and engineering knowledge advancement in about fatigue of material and structures. Hence, related approaches to be said as the subject contents, i.e. fatigue life prediction models, signal processing approaches, the implementation of segmentation and clustering methods towards fatigue data, as well as data classification that lead for pattern recognition technique. It is known from the literature about the selection of the appropriate approaches which were often based on the analyst's experience and preferences. By predicting the structure fatigue life, which needs only several variable and will automatically calculate, classify and optimise the severity of fatigue damage through the significant mathematical and experimental findings, leading to cost and time saving.

AB - The paper presents a review of the rational to perform the localised time-frequency fatigue damage feature classifications, which can be categorised as an alternative approach for fatigue life prediction that is relatively new in this research field. It is a good need to have a study in fatigue feature classification that lead to the formation of a new guideline and enable a design to the same reference level as well as high reliability towards the maximum usage. Consequently, this review paper emphasis on the concentration for performing the localised time-frequency feature classification approach as a scientific and engineering knowledge advancement in about fatigue of material and structures. Hence, related approaches to be said as the subject contents, i.e. fatigue life prediction models, signal processing approaches, the implementation of segmentation and clustering methods towards fatigue data, as well as data classification that lead for pattern recognition technique. It is known from the literature about the selection of the appropriate approaches which were often based on the analyst's experience and preferences. By predicting the structure fatigue life, which needs only several variable and will automatically calculate, classify and optimise the severity of fatigue damage through the significant mathematical and experimental findings, leading to cost and time saving.

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KW - Signal processing

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