Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold
Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold
复制标题
使用多小波去噪和数据驱动块阈值进行风力涡轮机故障检测
DOI：
10.1016/j.apacoust.2013.04.016
复制
发表时间：
2014-03-01
影响因子：
3.4
通讯作者：
He, Zhengjia
中科院分区：
文献类型：
--
作者：
Sun, Hailiang;Zi, Yanyang;He, Zhengjia
风力发电机组的快速发展引起了人们对降低运行和维护成本的关注。风力涡轮机的连续状态监测可以早期发现发电机故障，促进主动响应，最大限度地减少停机时间，最大限度地提高生产力。然而，风力发电机组早期故障的微弱特征往往淹没在设备噪声和环境噪声中。小波去噪是早期故障检测的重要工具，其效果主要取决于特征分离和噪声去除。多小波具有两个或多个多尺度函数和多小波函数。它们同时具有正交性、对称性、紧支撑和高消失矩等特性。数据驱动的块阈值利用最小Stein's无偏风险估计选择不同分解层次的最优块长度和阈值。提出了一种基于数据驱动的块阈值的多小波去噪技术。仿真实验和具有轻微内圈缺陷的滚动轴承的特征检测表明，该方法成功地检测出了早期故障的弱特征。爱思唯尔有限公司版权所有版权所有。
Rapid expansion of wind turbines has drawn attention to reduce the operation and maintenance costs. Continuous condition monitoring of wind turbines allows for early detection of the generator faults, facilitating a proactive response, minimizing downtime and maximizing productivity. However, the weak features of incipient faults in wind turbines are always immersed in noises of the equipment and the environment. Wavelet denoising is a useful tool for incipient fault detection and its effect mainly depends on the feature separation and the noise elimination. Multiwavelets have two or more multiscaling functions and multiwavelet functions. They possess the properties of orthogonality, symmetry, compact support and high vanishing moments simultaneously. The data-driven block threshold selected the optimal block length and threshold at different decomposition levels by using the minimum Stein's unbiased risk estimate. A multiwavelet denoising technique with the data-driven block threshold was proposed in this paper. The simulation experiment and the feature detection of a rolling bearing with a slight inner race defect indicated that the proposed method successfully detected the weak features of incipient faults. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.