2013
DOI: 10.1007/s12239-013-0089-9
|View full text |Cite
|
Sign up to set email alerts
|

Fatigue features extraction of road load time data using the S-transform

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
9
0

Year Published

2016
2016
2024
2024

Publication Types

Select...
7

Relationship

1
6

Authors

Journals

citations
Cited by 12 publications
(9 citation statements)
references
References 17 publications
0
9
0
Order By: Relevance
“…The distribution model is shown in Table 8. The Euler beta function B(a, b) and complementary error function erfc(x) in the PDF of Student's t-distribution and skew-normal distribution are shown as equations (22) and 23B a, b ð Þ=…”
Section: Fundingmentioning
confidence: 99%
See 1 more Smart Citation
“…The distribution model is shown in Table 8. The Euler beta function B(a, b) and complementary error function erfc(x) in the PDF of Student's t-distribution and skew-normal distribution are shown as equations (22) and 23B a, b ð Þ=…”
Section: Fundingmentioning
confidence: 99%
“…In order to preferably reproduce the failure mode and accurately predict the failure time of the cylindrical shock absorber used in the area, a compilation method of multi-axial loading spectrum was proposed formed on the road spectrum. 21 Abdullah et al 22 designed the algorithm development of a new fatigue data editing technique using S-T approach. The algorithm can be used to extract the fatigue damaging events, the combination of which produces a new edited signal that neglects the low-amplitude cycles.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, in many cases, the fatigue loading history is edited by removing these small amplitude cycles to produce representative and meaningful, yet economical testing, Stephens et al [2]. Several fatigue data editing techniques have been developed for use in the time domain analysis, Abdullah et al [3]. Some previous algorithms have been developed for eliminating low amplitude cycles in order to only retain the high amplitude cycles, El Ratal et al [4].…”
Section: Introductionmentioning
confidence: 99%
“…The filtering method does not shorten the signal because it does not provide the time-based information, Nizwan et al [5]. In addition, Abdullah et al [3] have developed a method for data editing to shorten the strain signal in the time-frequency domain. In fatigue data editing, the behaviour of extraction segments also needs to be studied because it contributes many bits of information that can improve fatigue life prediction.…”
Section: Introductionmentioning
confidence: 99%
“…According to their study, different road surfaces will result in different levels of fatigue damage to the components. Therefore, several important analyses, such as the Short-Time Fourier Transform (STFT), the Wavelet Transform and the S-Transform, have been created in the time-frequency domain to detect fatigue damage caused by road factors [6,7].…”
Section: Introductionmentioning
confidence: 99%