Novel method for residual strength prediction of defective pipelines based on HTLBO-DELM model

Miao, Xingyuan; Zhao, Hong

doi:10.1016/j.ress.2023.109369

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Lu et al [21] used principal component analysis combined with a multi-objective optimization vector machine to predict the residual strength of the pipeline. Miao and Zhao [22] proposed a method of deep extreme machine learning to predict the residual strength of defective pipes. Meng et al [23] proposed an adaptive Kriging model to improve the efficiency of optimal design for offshore wind turbines.…”

Section: Introductionmentioning

confidence: 99%

Residual Strength Modeling and Reliability Analysis of Wind Turbine Gear under Different Random Loadings

Gao,

Liu,

Yuan

et al. 2023

Mathematics

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A novel method is proposed to investigate the pattern of variation in the residual strength and reliability of wind turbine gear. First, the interaction between loads and the effect of the loading sequence is considered based on the fatigue damage accumulation theory, and a residual strength degradation model with few parameters is established. Experimental data from two materials are used to verify the predictive performance of the proposed model. Secondly, the modeling and simulation of the wind turbine gear is conducted to analyze the types of fatigue failures and obtain their fatigue life curves. Due to the randomness of the load on the gear, the rain flow counting method and the Goodman method are employed. Thirdly, considering the seasonal variation of load, the decreasing trend of gear fatigue strength under multistage random load is calculated. Finally, the dynamic failure rate and reliability of gear fatigue failure under multistage random loads are analyzed. The results demonstrate that the randomness of residual strength increases with increasing service time. The seasonality of load causes fluctuations in the reliability of gear, providing a new idea for evaluating the reliability of the wind turbine gear.

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Section: Introductionmentioning

confidence: 99%