基于突变点检测与改进粒子滤波的风力机回转支承剩余寿命预测

刘明君; 王双川; 董增寿

doi:doi:10.19912/j.0254-0096.tynxb.2024-1354

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基于突变点检测与改进粒子滤波的风力机回转支承剩余寿命预测

更新时间：2026-03-06

- 基于突变点检测与改进粒子滤波的风力机回转支承剩余寿命预测
- Vol. 47, Issue 2, Pages: 408-417(2026)
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1354
  CLC： TK83
- Online First：05 March 2026，
  
  Published：05 March 2026
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刘明君, 王双川, 董增寿. 基于突变点检测与改进粒子滤波的风力机回转支承剩余寿命预测[J]. 2026, 47(2): 408-417.
DOI：

刘明君, 王双川, 董增寿. 基于突变点检测与改进粒子滤波的风力机回转支承剩余寿命预测[J]. 2026, 47(2): 408-417. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1354.

摘要

风力机回转支承在运行过程中易受恶劣环境及突发工况等因素影响导致难以准确估计其剩余寿命

因此

提出一种结合Wiener过程和改进粒子滤波的剩余寿命预测方法。首先

通过突变点检测方法识别退化过程中的突变点。其次

采用非线性Wiener过程构建带突变点的状态空间模型

并对传统粒子滤波进行改进

推导出剩余寿命的概率密度函数及参数更新公式。最后

以风力机回转支承的数据为例

验证所提方法的有效性和优越性。

Abstract

A remaining useful life prediction method based on the Wiener process and an improved particle filter is proposed to address the challenge of accurately estimating the remaining life of wind turbine slewing bearings due to their accelerated degradation under harsh environmental conditions and sudden operational scenarios. Firstly

the mutation point in the degradation process is identified using a mutation point detection method

and the traditional particle filter algorithm is improved. Secondly

the state space model with mutation points is constructed using a nonlinear Wiener process

and the probability density function of the remaining useful life and the parameter update formula are derived. Finally

data from wind turbine slewing bearings are used as an example to verify the effectiveness and superiority of the proposed method.

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references

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