Wind turbine blade bearing fault detection with Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm
Wind turbine blade bearing fault detection with Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm
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DOI：
10.1016/j.renene.2022.09.030
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发表时间：
2022-09
期刊：
影响因子：
8.7
通讯作者：
C. Zhang;Zepeng Liu;Long Zhang
中科院分区：
文献类型：
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作者：
C. Zhang;Zepeng Liu;Long Zhang
作为风力涡轮机的关键支撑和旋转部件，叶片轴承需要特殊的健康监测才能在实际工业条件下安全运行。风力机叶片承载状态监测的主要难点之一是在脉动慢速重载条件下产生的噪声信号。这是因为叶片轴承转速受到叶片翻转和外界干扰的影响，而且这种影响是时变的。本文提出了一种新的滤波方法，即贝叶斯和自适应卡尔曼增广拉格朗日（BAKAL）方法。该方法采用基于贝叶斯增广拉格朗日（BAL）框架的粗搜索和细搜索两步搜索来处理过滤过程。此外，模型构建还考虑了线性和非线性效应及其稀疏性。最后，通过对信号在阶域的采样处理频谱上的模糊问题，提高故障诊断的性能。本文提出的BAKAL算法在重载条件下的近似定速和变速实验中得到了严格的验证。实验使用的是一种具有自然缺陷的工业和旋转风力涡轮机叶片轴承，该轴承已在实际风力发电厂服役超过15年。实验结果证明了该方法的有效性。
As a critically supporting and rotational component for wind turbines, blade bearings need special health monitoring for safe operation in actual industrial conditions. One of the main difficulties of the wind turbine blade bearing condition monitoring is noisy signals generated under fluctuating slow speed with heavy loads. This is because blade bearing rotation speed is influenced by blade flipping and external disturbances, and this influence is time-varying. This paper proposes a new method, Bayesian and Adapted Kalman Augmented Lagrangian (BAKAL), to filter the signal under this time-varying condition. The new method uses a two-step search (coarse and fine search) to deal with the filtering process based on Bayesian Augmented Lagrangian (BAL) framework. In addition, both linear and nonlinear effects and their sparsity are considered for model construction. Finally, the smearing problem in the frequency spectrum is dealt with through signal resample in the order domain for superior performance of fault diagnosis. The proposed BAKAL algorithm is strictly validated in several experiments under approximately fixed speed and variable speed within the condition of heavy loadings. The experiments use an industrial and rotational wind turbine blade bearing with natural defects, which has been served in an actual wind power plant for over 15 years. The experimental results demonstrate the effectiveness of the proposed method.