基于迁移学习的海上风电机组轴承早期故障预警策略
Early fault warning strategy for offshore wind turbine bearings based on transfer learning
辛治铖 1汪隆君 1刘沈全1
作者信息
- 1. 华南理工大学,广东 广州 510000
- 折叠
摘要
针对海上风电机组工况不一、机组轴承早期故障预警误报警多的问题,文章建立了一种基于迁移学习的海上风电机组轴承早期故障预警方法.首先,采用短时傅立叶变换提取振动信号时频域特征,归一化后形成预处理样本;然后,在卷积自编码器的目标函数中添加支持向量数据描述正则项和最大均值差异正则项,约束特征分布的同时获得轴承在不同工况正常状态的公共特征中心;最后,计算在线样本特征与公共特征中心的欧氏距离,构建轴承健康指标序列,引入增广迪基-富勒检验(ADF)方法作平稳性分析,捕捉序列突变点,最终实现对海上风电机组轴承早期故障预警.在XJTU-SY轴承数据集上的验证表明,所提方法误报警少、准确度高,具有更好的检测稳定性.
Abstract
A transfer learning-based early fault warning method for offshore wind turbine bearings is established to address the problems of varying operating conditions of offshore wind turbines and many false alarms for early fault warning of turbine bearings.The method uses the short-time Fourier transform to extract the time-frequency domain features of the vibration signals,which are normalised to form pre-processed samples.The objective function of the convolutional autoencoder is supplemented with a support vector data description regular term and a maximum mean discrepancy regular term to constrain the feature distribution while obtaining the common features center of the bearings in normal state under different operating conditions.The Euclidean distance between the online sample features and the common feature center is calculated to construct bearing health indicator sequence,and the ADF(Augmented Dickey-Fuller)test is introduced to perform stationarity analysis and capture the sequence mutation points,which finally realize the early fault warning of bearings in offshore wind turbines.The validation on the XJTU-SY bearing dataset showed that the proposed method has fewer false alarms,high accuracy and better detection stability.
关键词
早期故障预警/平稳性检验/迁移学习/轴承/海上风电Key words
early fault warning/stability test/transfer learning/bearing/offshore wind引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据