Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions
Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions
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DOI：
10.1109/tia.2021.3058557
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发表时间：
2021-05-01
影响因子：
4.4
通讯作者：
Zhang, Long
中科院分区：
文献类型：
--
作者：
Liu, Zepeng;Yang, Boyuan;Zhang, Long
本文利用声发射分析技术对一台工业级低速风力涡轮机叶片轴承进行故障诊断。声发射分析的主要挑战是故障信号中夹杂着强噪声。因此，本文的目标是对原始声发射信号进行滤波，提取微弱的故障信号。为了实现这一目标，一般的线性和非线性自回归（GLNAR）模型首先开发利用AE信号的非线性特性。然后，稀疏增广拉格朗日（SAL）算法被应用于学习所建立的GLNAR模型和过滤的原始AE信号。SAL的特点是它是一种新的稀疏表示技术，它可以将原始的滤波问题转化为一些子优化问题，这些子问题可以单独求解。最后，当叶片轴承以波动的速度旋转时，对滤波后的信号进行重新采样，从而可以在阶次域中诊断轴承故障类型。建议的诊断框架进行了验证，在几个实验下随时间变化的低速和重叶片负载条件。结果表明，本文提出的方法是有效的，准确的.
This article uses acoustic emission (AE) analysis to diagnose an industrial-scale and slow-speed wind turbine blade bearing. The main challenge for AE analysis is that the fault signals are mingled with heavy noise. As a result, the objective of this article is to filter the raw AE signals and extract weak fault signals. To achieve this goal, a general linear and nonlinear auto-regressive (GLNAR) model is first developed to exploit the nonlinear characteristics of the AE signals. Then, the sparse augmented Lagrangian (SAL) algorithm is applied to learn the built GLNAR model and filter the raw AE signals. The characteristics of SAL is that it is a novel sparse representation technique which can convert the original filtering problem into a number of suboptimization problems, and these subproblems can be solved separately. Finally, as the blade bearing rotates at fluctuating speeds, the filtered signals are resampled so that the bearing fault type can be diagnosed in the order domain. The proposed diagnostic framework was validated in several experiments under time-varying low-speed and heavy-blade-load conditions. The results indicate that our proposed methods are effective and accurate.