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Based on the Morlet wavelet transformation and Wigner-Ville distribution (WVD), we present a wind turbine fault diagnosis method in this paper. Wind turbine can be damaged by moisture absorption, fatigue, wind gusts or lightening strikes. Due to this reason, there is an increasing need to monitor the health of these structures. Vibration analysis is the best-known technology applied in wind turbine condition monitoring, in which the time-frequency analysis techniques such as Wigner-Ville distribution (WVD) are widely used. Theoretically WVD has an infinite resolution in time-frequency domain. For early wind turbine fault signals, however, there are two main difficulties in WVD analysis. One is strong noise signals in the background and the other is cross terms in WVD itself. In this paper, continuous wavelet transformation (CWT) is employed to filter useless noise in raw vibration signals, and auto terms window (ATW) function is used to suppress the cross terms in WVD. In the CWT de-noising process, the Morlet wavelet, whose shape is similar to mechanical shock signals, is chosen to perform CWT on the raw vibration signals. The appropriate scale parameter for CWT is optimized by the cross validation method (CVM). An ATW based on the Smoothed Pseudo Wigner-Ville distribution (SPWVD) spectrum is taken to be a window function to suppress the cross terms in WVD. The new method can not only remove cross terms faraway from the auto terms, but also keep high energy close to every instantaneous frequency, the virtues such as high time-frequency resolution, and good energy aggregation etc. The wind turbine gear fault diagnosis experiment results indicate that the proposed method has a good de-nosing performance and is effective in suppressing the cross terms and extracting fault feature. 相似文献
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基于AR-Hankel矩阵的风力发电机早期故障诊断方法研究 总被引:1,自引:0,他引:1
《可再生能源》2016,(1)
针对风力发电机故障早期阶段,故障特征不明显,继电保护装置检测不到相关电气量异常的问题,文章提出了基于AR模型、Hankel矩阵和奇异值分解的风力发电机早期故障诊断方法。该方法先对主轴径向振动信号进行总体平均经验模式分解,再按照互相关准则选择若干个固有模态函数建立AR模型,然后对自回归系数构建Hankel矩阵并作奇异值分解,将奇异值作为故障特征输入支持向量机判断发电机的运行状态。试验结果表明,该方法能对直驱风力发电机正常运行、定子线圈匝间短路、发电机主轴偏心、发电机轴承磨损4种状态进行准确诊断。 相似文献
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对燃气轮机润滑油系统常见故障原因进行了分析,并结合专家知识建立了润滑油系统故障知识库;在此基础上将RBF人工神经网络引入燃气轮机装置故障诊断中来,由于采用了一种动态RBF网设计方法,使得神经网络的规模较小同时具有较高的泛化能力,提高了神经网络的诊断速度及准确性. 相似文献
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基于机车故障诊断专家系统的故障树优化设计 总被引:2,自引:0,他引:2
针对基于故障树的机车故障诊断专家系统,通过引入二叉故障树的概念,将传统故障树合成为一棵二叉故障树模型,并以此为基础,对其知识库的存储和故障诊断方式进行了研究,达到了优化系统的目的。 相似文献
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针对风电机组故障信号的非平稳性以及故障与征兆的非线性映射导致的故障识别困难问题,提出了改进型的节点重构小波包频带能量谱与PNN(概率神经网络)的联合故障诊断新方法。文章深入分析了传统小波包频带错乱的问题,借助傅里叶变换与傅里叶逆变换改进了小波包,消除了小波包频带错乱的缺陷。首次采用改进型小波包提取故障信号特征量作为PNN的输入,然后利用PNN快速准确的非线性映射能力进行故障诊断。最后,采用风力发电机故障试验台的故障轴承的实际数据对所提方法进行验证,结果表明,所提方法可行且有效。 相似文献
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《可再生能源》2019,(11):1720-1725
为了及时有效地检测出风电机组发生的具体故障,同时克服传统故障诊断方法的局限性,文章提出一种基于改进深度森林算法的风电机组故障诊断方法。首先,利用有效的数据预处理方法处理SCADA原始数据并提取故障特征;然后,基于深度森林算法对风电机组具体故障进行诊断,同时,针对深度森林算法在故障诊断领域存在的缺陷,对算法提出改进;最后,利用河北某风场1.5 MW风电机组实际运行数据对文章提出的故障诊断算法进行验证,通过正确率、AUC等指标验证了所提故障诊断算法相比传统机器学习算法的有效性和优越性。该研究为风电机组运行和维修提供了依据,同时也为故障诊断领域提供了新的方法和思路。 相似文献
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变速风力发电机变流器故障诊断方法 总被引:2,自引:0,他引:2
大型变距变速风力发电机组状态的监测与故障的诊断是保证机组长期稳定运行和安全发电的关键。文章针对变速风力发电机组中的变流器电路模型非线性强的特点,利用神经网络非线性映射特性,提出了采用基于波形直接分析的BP神经网络故障诊断方法。该方法能动态监视风力发电机变流器并网电路的工作状态,实时在线进行故障诊断和快速分析,确定变流器故障的部位和性质,可缩短风力发电机的故障停机时间。实际运行结果表明,该方法对变速风力发电机组的状态监测与故障诊断是有效的。 相似文献
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Jae Yoon David He Brandon Van Hecke Thomas J. Nostrand Junda Zhu Eric Bechhoefer 《风能》2016,19(9):1733-1747
Planetary gearboxes (PGBs) are widely used in the drivetrain of wind turbines. Any PGB failure could lead to a significant breakdown or major loss of a wind turbine. Therefore, PGB fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and lifespan of wind turbines. The wind energy industry currently utilizes vibratory analysis as a standard method for PGB condition monitoring and fault diagnosis. Among them, the vibration separation is considered as one of the well‐established vibratory analysis techniques. However, the drawbacks of the vibration separation technique as reported in the literature include the following: potential sun gear fault diagnosis limitation, multiple sensors and large data requirement, and vulnerability to external noise. This paper presents a new method using a single vibration sensor for PGB fault diagnosis using spectral averaging. It combines the techniques of enveloping, Welch's spectral averaging, and data mining‐based fault classifiers. Using the presented approach, vibration fault features for wind turbine PGB are extracted as condition indicators for fault diagnosis and condition indicators are used as inputs to fault classifiers for PGB fault diagnosis. The method is validated on a set of seeded localized faults on all gears: sun gear, planetary gear, and ring gear. The results have shown a promising PGB fault diagnosis performance with the presented method. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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