Fault diagnosis method for wind power gearbox based on IPSO-FNN

BAI Ruifei; ZHANG Wei; ZHAO Doudou

doi:10.13873/J.1000-9787(2021)01-0125-04

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Fault diagnosis method for wind power gearbox based on IPSO-FNN

更新时间：2025-09-11

- Fault diagnosis method for wind power gearbox based on IPSO-FNN
- Vol. 40, Issue 1, Pages: 125-128(2021)
- 作者机构：
  
  河南理工大学电气工程与自动化学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2021)01-0125-04
  CLC： TP18;TM315
- Published：2021
- 稿件说明：
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[1]白瑞飞,张伟,赵豆豆.基于IPSO-FNN的风电齿轮箱故障诊断方法[J].传感器与微系统,2021,40(01):125-128.

BAI Ruifei, ZHANG Wei, ZHAO Doudou. Fault diagnosis method for wind power gearbox based on IPSO-FNN[J]. 2021, 40(1): 125-128.
[1]白瑞飞,张伟,赵豆豆.基于IPSO-FNN的风电齿轮箱故障诊断方法[J].传感器与微系统,2021,40(01):125-128. DOI： 10.13873/J.1000-9787(2021)01-0125-04.

BAI Ruifei, ZHANG Wei, ZHAO Doudou. Fault diagnosis method for wind power gearbox based on IPSO-FNN[J]. 2021, 40(1): 125-128. DOI： 10.13873/J.1000-9787(2021)01-0125-04.

摘要

在对风电齿轮箱故障特征提取基础上

利用模糊神经网络（FNN）对齿轮箱故障诊断系统进行网络建模;为了提高网络学习算法效率

采用改进粒子群优化（IPSO）算法对网络参数进行学习。引入适应度方差表征粒子状态

对早熟粒子进行差分进化操作

改善粒子群的多样性。对惯性权重、学习因子进行改进

平衡算法的全局搜索和局部开发能力

提高算法性能。经仿真实验研究表明:所提出的故障诊断方法与FNN

PSO-FNN方法相比

具有较高的诊断精度和较快的收敛速度。

Abstract

On the basis of wind power gearbox fault feature extraction

fuzzy neural network（FNN） is used for network madeling of gearbox fault diagnosis system.In order to improve the efficiency of network learning algorithm

improved particle swarm optimization（PSO） algorithm is used to learn network parameters.Fitness variance is introduced to characterize the state of particles

and differential evolution is applied to premature particles to improve the diversity of particle swarm.The inertia weight and learning factor are improved to balance the global search and local development ability of the algorithm and improve the performance of the algorithm.The simulation results show that the proposed fault diagnosis method has higher diagnostic accuracy and faster convergence speed than FNN and PSO-FNN methods.

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references

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