针对现有风力机叶片两阶段退化可靠性评估方法的局限性,即忽略失效阈值随机性和退化过程变点准确性,提出一种新方法。该方法基于非线性维纳（Wiener）退化过程,考虑失效阈值随机性对不同阶段的影响。引入修正的赤池信息准则（AICc）确定最优变点位置,并进行区间估计。基于变点位置采用极大似然估计法确定两阶段漂移系数和扩散系数的估计值,然后通过疲劳裂纹扩展仿真实验得到相关数据,建立叶片退化的可靠性模型。同时,为验证模型预测的准确性,利用自适应算法更新后的风力机叶片数据进行实例分析。结果表明,失效阈值的随机性和变点准确性对退化建模具有显著影响,能够有效提高可靠性评估的准确性。
Abstract
To address the limitations of existing two-stage degradation reliability assessment methods for wind turbine blades, specifically the neglect of failure threshold randomness and the accuracy of changepoint detection in the degradation process, a novel approach is proposed. This method is based on a nonlinear Wiener degradation process and considers the impact of random failure thresholds across different stages. The corrected akaike information criterion （AICc） is introduced to determine the optimal changepoint location, followed by interval estimation. Maximum likelihood estimation is employed to determine the drift and diffusion coefficients for the two stages based on the identified changepoint. Subsequently, a reliability model for blade degradation is established using data obtained from fatigue crack propagation simulation experiments. To validate the accuracy of the model predictions, an empirical analysis is conducted using updated wind turbine blade data via an adaptive algorithm. The results demonstrate that accounting for failure threshold randomness and changepoint accuracy significantly impacts degradation modeling, thereby enhancing the precision of reliability assessments.
关键词
风力机叶片 /
退化 /
失效 /
极大似然估计 /
疲劳裂纹扩展 /
自适应算法 /
可靠性
Key words
wind turbine blades /
degradation /
failure /
maximum likelihood estimation /
fatigue crack propagation /
adaptive algorithms /
reliability
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参考文献
[1] 马航, 毕俊喜, 葛新宇, 等. 基于优化等寿命疲劳极限模型的风力机叶片寿命预测[J]. 太阳能学报, 2023, 44(10): 362-369.
MA H, BI J X, GE X Y, et al.Service life prediction of wind turbine blade based on optimizated constant life fatigue limit model[J]. Acta energiae solaris sinica, 2023, 44(10): 362-369.
[2] 胡姚刚, 刘怀盛, 时萍萍, 等. 风电机组偏航系统故障诊断与寿命预测综述[J]. 中国电机工程学报, 2022, 42(13): 4871-4884.
HU Y G, LIU H S, SHI P P, et al.Overview of fault diagnosis and life prediction for wind turbine yaw system[J]. Proceedings of the CSEE, 2022, 42(13): 4871-4884.
[3] 王国锋, 曹增欢, 冯海生, 等. 基于多阶段退化建模的谐波减速器实时可靠性评估与寿命预测[J]. 天津大学学报(自然科学与工程技术版), 2022, 55(2): 122-132.
WANG G F, CAO Z H, FENG H S, et al.Real-time reliability evaluation and life prediction of harmonic reducer based on multistage degradation modeling[J]. Journal of Tianjin University (science and technology), 2022, 55(2): 122-132.
[4] GADDAFEE M, CHINCHANIKAR S.An experimental investigation of cutting tool reliability and its prediction using weibull and gamma models: a comparative assessment[J]. Materials today: proceedings, 2020, 24: 1478-1487.
[5] 王小林, 郭波, 程志君. 基于分阶段Wiener-Einstein过程设备的实时可靠性评估[J]. 中南大学学报(自然科学版), 2012, 43(2): 534-540.
WANG X L, GUO B, CHENG Z J.Real-time reliability evaluation of equipment based on separated-phase Wiener-Einstein process[J]. Journal of Central South University (science and technology), 2012, 43(2): 534-540.
[6] 陈伟, 雷欢, 裴婷婷, 等. 基于退化轨迹和Wiener模型的光伏组件剩余寿命预测方法[J]. 太阳能学报, 2023, 44(7): 175-181.
CHEN W, LEI H, PEI T T, et al.Remaining life prediction method of photovoltaic modules based on degradation trajectoryand Wiener model[J]. Acta energiae solaris sinica, 2023, 44(7): 175-181.
[7] 王延忠, 鄂世元, 谢斌. 基于非齐次Gamma过程的滚动轴承磨损随机过程建模方法[J]. 新技术新工艺, 2023(4): 74-76.
WANG Y Z, E S Y, XIE B. Modeling method of rolling bearing wear stochastic process based on inhomogeneous Gamma process[J]. New technology & new process, 2023(4): 74-76.
[8] 马强, 安宗文, 寇海霞. 考虑随机失效阈值的退化数据建模分析方法[J]. 兰州理工大学学报, 2017, 43(5): 40-46.
MA Q, AN Z W, KOU H X.Degraded data modeling and analysis method taking account of random failure threshold[J]. Journal of Lanzhou University of Technology, 2017, 43(5): 40-46.
[9] 王玺, 胡昌华, 任子强, 等. 基于非线性Wiener过程的航空发动机性能衰减建模与剩余寿命预测[J]. 航空学报, 2020, 41(2): 195-205.
WANG X, HU C H, REN Z Q, et al.Performance degradation modeling and remaining useful life prediction for aero-engine based on nonlinear Wiener process[J]. Acta aeronautica et astronautica sinica, 2020, 41(2): 195-205.
[10] 鄢伟安, 宋保维, 段桂林, 等. 基于两阶段维纳退化过程的液力耦合器可靠性评估[J]. 系统工程与电子技术, 2014, 36(9): 1882-1886.
YAN W A, SONG B W, DUAN G L, et al.Reliability evaluation of LCD based on two-phase Wiener degradation process[J]. Systems engineering and electronics, 2014, 36(9): 1882-1886.
[11] 姜洋. 基于两阶段Gamma过程的机械产品剩余寿命预测研究[J]. 机电工程, 2021, 38(6): 802-806.
JIANG Y.Remaining useful lifetime prediction of the mechanical products based on the two-phase Gamma process[J]. Journal of mechanical & electrical engineering, 2021, 38(6): 802-806.
[12] 张奡, 王治华, 赵振平, 等. 两阶段非线性Wiener过程退化建模与可靠性分析[J]. 系统工程与电子技术, 2020, 42(4): 954-959.
ZHANG A, WANG Z H, ZHAO Z P, et al.Two-phase nonlinear Wiener process for degradation modeling and reliability analysis[J]. Systems engineering and electronics, 2020, 42(4): 954-959.
[13] 高新, 叶楠, 周坤, 等. 考虑测量误差的两阶段Wiener建模与可靠性分析[J]. 中国机械工程, 2023, 34(9): 1067-1076.
GAO X, YE N, ZHOU K, et al.Two stage Wiener modeling and reliability analysis considering measurement errors[J]. China mechanical engineering, 2023, 34(9): 1067-1076.
[14] WEI M H, CHEN M Y, ZHOU D H.Multi-sensor information based remaining useful life prediction with anticipated performance[J]. IEEE transactions on reliability, 2013, 62(1): 183-198.
[15] WANG J H, ZHANG L A, HUANG X M, et al.Initiation mechanism of transverse cracks in wind turbine blade trailing edge[J]. Energy engineering, 2022, 119(1): 407-418.
[16] WEN Y X, WU J G, DAS D, et al.Degradation modeling and RUL prediction using Wiener process subject to multiple change points and unit heterogeneity[J]. Reliability engineering & system safety, 2018, 176: 113-124.
基金
国家自然科学基金（52165019）; 2023年自治区重点研发和成果转化计划（科技合作）项目（2023KJHZ0008）
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