The Load Cycle Amplitude Model: An Efficient Time-Domain Extrapolation Technique for Non-Stationary Loads in Agricultural Machinery

Zihan Yang, Xuke Liu, Zhenghe Song () and Hanting Liu
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Zihan Yang: College of Engineering, China Agricultural University, Beijing 100083, China
Xuke Liu: College of Engineering, China Agricultural University, Beijing 100083, China
Zhenghe Song: College of Engineering, China Agricultural University, Beijing 100083, China
Hanting Liu: Luoyang Smart Agricultural Equipment Institute Co., Ltd., Luoyang 471000, China

Agriculture, 2024, vol. 14, issue 12, 1-16

Abstract: In traditional time-domain extrapolation methods, the peak over threshold (POT) model is unable to accurately identify large load cycles in the load time history, resulting in distorted extrapolation results, particularly when addressing non-stationary loads. To address this problem, this paper proposes a time-domain extrapolation method based on the load cycle amplitude (LCA) model. The core of the method involves using load cycle amplitude features extracted from the measured loads as the basis for modelling, rather than extreme turning points based on threshold extraction. This approach prevents the load’s time-domain characteristics from compromising the accuracy of the extrapolation results. The case analysis results demonstrate that the extrapolation method based on the LCA model achieves more reliable results with both non-stationary and stationary loads. Furthermore, the streamlined modelling process results in reductions of 10.63% and 20.84% in the average computing time for the algorithm when addressing stress and vibration loads, respectively. The LCA model proposed in this paper further facilitates the integration of time-domain extrapolation methods into reliability analysis software.

Keywords: load spectrum; time-domain extrapolation; non-stationary load; extreme value theory (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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