Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis
Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis
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DOI：
10.1109/tim.2020.2969062
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发表时间：
2020-09-01
影响因子：
5.6
通讯作者：
Zhang, Long
中科院分区：
文献类型：
--
作者：
Liu, Zepeng;Wang, Xuefei;Zhang, Long
风力涡轮机叶片轴承通常在恶劣的环境下运行，很容易损坏，导致涡轮机失去控制，进而导致能源生产的减少。然而，对于风力涡轮机叶片轴承的状态监测和故障诊断（CMFD），主要困难之一是叶片轴承的旋转速度非常慢（小于5 r/min）。在过去的几年里，声发射（AE）分析已被用于进行轴承CMFD。本文介绍的结果，反映了潜在的AE分析诊断低速风力涡轮机叶片轴承。为了进行这项实验，本研究使用了一个15年自然损坏的工业低速叶片轴承。然而，由于非常低的转速条件，故障信号非常弱，并被严重的噪声干扰所掩盖。为了对原始声发射信号进行去噪处理，提出了一种新的倒谱编辑方法--基于离散/随机分离的倒谱编辑提升法（DRS-CEL），用于从原始声发射信号中提取微弱故障特征，其中DRS用于倒谱编辑。随后，利用形态学包络分析进一步滤除DRS-CEL泄漏的残余噪声，并对去噪信号进行解调，从而在频域推断出具体的轴承故障类型。通过比较几种方法和相关研究，验证了DRS-CEL和形态分析相结合的诊断框架，这为风电场应用提供了一个有前途的解决方案。
Wind turbine blade bearings are often operated in harsh circumstances, which may easily be damaged causing the turbine to lose control and to further result in the reduction of energy production. However, for condition monitoring and fault diagnosis (CMFD) of wind turbine blade bearings, one of the main difficulties is that the rotation speeds of blade bearings are very slow (less than 5 r/min). Over the past few years, acoustic emission (AE) analysis has been used to carry out bearing CMFD. This article presents the results that reflect the potential of the AE analysis for diagnosing a slow-speed wind turbine blade bearing. To undertake this experiment, a 15-year-old naturally damaged industrial and slow-speed blade bearing is used for this study. However, due to very slow rotation speed conditions, the fault signals are very weak and masked by heavy noise disturbances. To denoise the raw AE signals, we propose a novel cepstrum editing method, discrete/random separation-based cepstrum editing liftering (DRS-CEL), to extract weak fault features from raw AE signals, where DRS is used to edit the cepstrum. Thereafter, the morphological envelope analysis is employed to further filter the residual noise leaked from DRS-CEL and demodulate the denoised signal, so the specific bearing fault type can be inferred in the frequency domain. The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.