One-stage extraction four-point algorithm for rainflow cycle counting in fatigue life estimation
PurposeIn order to improve the computation efficiency of the four-point rainflow algorithm, a one-stage extraction four-point rainflow algorithm is proposed based on a novel data preprocessing method.Design/methodology/approachIn this new algorithm, the procedure of cycle counting is simplified by i...
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| Published in | Engineering computations Vol. 40; no. 1; pp. 126 - 148 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bradford
Emerald Publishing Limited
14.02.2023
Emerald Group Publishing Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0264-4401 1758-7077 |
| DOI | 10.1108/EC-06-2022-0385 |
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| Summary: | PurposeIn order to improve the computation efficiency of the four-point rainflow algorithm, a one-stage extraction four-point rainflow algorithm is proposed based on a novel data preprocessing method.Design/methodology/approachIn this new algorithm, the procedure of cycle counting is simplified by introducing the data preprocessing method. The high efficiency of new algorithm makes it a preferable candidate in fatigue life online estimation of structural health monitoring systems.FindingsAccording to the data preprocessing method, in the process of cycle extraction, all equivalent cycles can be extracted at just one stage instead of two stages in the four-point rainflow algorithm, where the cycle extraction has to be performed from the doubled residue. Besides, there are no residues in the new algorithm. The extensive numerical simulation results demonstrate that the accuracy of new algorithm is the same as that of the four-point rainflow algorithm. Moreover, a comparative study based on a long input data sequence shows that the computation efficiency of the new algorithm is 42% higher than that of the four-point rainflow algorithm.Originality/valueThis merit of new algorithm makes it preferable in some application scenarios where fatigue life estimation needs to be accomplished online based on massive measured data. And it may attribute to preprocessing of input data sequence before data processing, which provides beneficial guidance to improve the efficiency of existing algorithms. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0264-4401 1758-7077 |
| DOI: | 10.1108/EC-06-2022-0385 |