摘要
在目前国内外能源短缺和环境污染问题日益严重的情况下,大力开发和利用风能是解决上述问题的有效途径和必然趋势之一。在风电产业快速发展的同时,风力发电机组的安全、稳定运行逐渐引起国内外的高度重视。在双馈型和半直驱型风力发电机组中,由齿轮箱轴承故障引起的停机时间最长,对其故障进行研究具有重要的理论指导价值和工程应用意义。因此,本文以风力发电机组齿轮箱滚动轴承为研究对象,采用先进技术方法对其进行故障类型、故障部位和故障程度的诊断,为提高风力发电机组的利用率,降低风场的运行维护费用提供重要理论支撑和技术保证。
本文的主要研究内容如下：
1.提出了一种基于数学形态滤波的小波特征频带滚动轴承早期故障诊断方法。首先利用小波-数学形态联合降噪算法对信号进行预处理；再建立滚动轴承故障振动信号模型,以减小诊断的盲目性,提高诊断精确度；最后根据理论推导分析出的结论：当滚动轴承发生故障时,其振动信号相应故障特征频带能量显著增大,来判定小波分解结果中包含故障信息的特征频带,将包含在其中的周期性冲击特征和故障调制信息分离出来,从而判定故障类型和故障部位。
2.提出了一种基于峭度的EMD (Empirical Mode Decomposition)方法迭代截止规则。该规则利用峭度对冲击成分敏感,进而可反映幅值分布形状变化,弥补了现有规则依赖于某一经验值的缺陷；同时该规则考虑了IMF (Instrinsic Mode Function)分量的定义、分解过程的正交性和完备性,使分解得到的IMF分量更接近理想值,具有更小的正交性指标。
3.在上述第2点迭代截止规则优化后的EMD方法基础上,提出了一种新型滚动轴承故障部位及程度在线诊断方法。该方法首先提取可准确表征故障信息的特征量,再计算待测样本和预先设定的各故障模式标准样本之间的J-散度(J-divergence)和KL-散度(Kullback-Leibler divergence)值；利用散度值可表征故障点所在位置,以及散度值的变化可跟踪故障程度变化的特性,来判断故障部位及程度。
4.在上述第2点迭代截止规则优化后的EMD方法基础上,提出了一种新型滚动轴承故障部位及程度自适应聚类方法。该方法首先通过优化EMD方法提取故障特征量,然后针对原始K-均值聚类算法的两个缺陷,即聚类数目和聚类中心不易确定,及易受孤立点的影响,应用主成分分析和霍特林T2统计量进行解决,提出了自适应K-均值聚类算法,从而使该故障诊断方法可自适应的将样本集按故障部位和故障程度进行分类。
5.针对目前风力发电机组缺乏一个面向整机重要部件关键参数的综合性监测系统,且现有在线监测系统的分析和诊断功能较薄弱的现状,本文设计并实现了一种风力发电机组在线故障预警和诊断一体化系统,分别从系统结构、系统功能和一体化系统特点三个方面,对整个系统的硬件和软件实现进行了阐述；分析了故障预警系统的工作原理和故障诊断方法的步骤,并重点对故障诊断系统自学习能力的具体功能和实现方法进行了说明。
With the energy shortage and environment pollution becoming more and more serious, one of effective ways to solve the problemes is to develop wind energy. While maintaining the high-speed development of wind power industry, it is worthwhile to pay more attention to the safety operation of wind turbine. The bearings in gearbox have the longest downtime compared to the other components. So the research on its failure has important theoretical value and engineering significance. The object of study in this paper is rolling element bearings of wind turbine gearbox. The fault diagnosis of bearings is important, which can reduce the cost of wind plant and promote the operation efficiency of wind turbines.
The major contributions of this thesis are summarized as follows:
1. A new approach based on mathematical morphological filter and wavelet is presented, which is to identify bearing faults via vibration while the faults is still in an incipient stage. First, this approach considers the removal of random pulse and white noise, and then a model-based vibration is established for decreasing the blindness and increasing the accuracy of fault diagnosis. Based on the above model, we can deduce that there is a significant increase in the energy of fault characteristic band, when single-point defects occur on a bearing surface. Consequently, the characteristic fault frequencies which are separated from fault characteristic band are utilized to obtain the style and location of defect.
2. In order to solve the disadvantage of the dependence of empirical value of existing IMF stop criterion, a new stop criterion based on kurtosis is proposed. Given the sensitivity of kurtosis to pulses, it can reveal the distribution of amplitude. At the same time, this criterion has considered the definition of IMF, the index of orthogonality and completeness so that the IMF component has less error and better orthogonality.
3. A new technique based on the optimized EMD and divergence is proposed. It can diagnose the location and degree of defects. The fault signatures of bearings are extracted firstly. Then the J-divergences and KL-divergence between test sample and standard sample are computed. Finally, the divergence and its variation can determine the location and the severity degree of fault, respectively.
4. A new approach based on the optimized EMD and adaptive K-means clustering is proposed. It can diagnose the location and degree of defects. The fault signatures of bearings are extracted firstly by optimized EMD. The Principal Component Analysis and Holtelling T2-statistic are applied to improve the weaknesses of K-means clustering, that are, the determination of cluster number and cluster center is difficult and there exists the influence of isolated points. So the adaptive K-means clustering is proposed. This approach can adaptively identify the data sets, depending on the location and the severity level of fault.
5. Now the available system which can monitor the key operating parameters of all the important parts of wind turbine is few, and fault diagnosis in the existing system is weak. An integrated system is designed, which can combine the on-line fault warning and fault diagnosis. The hardware and software implementations of system are illustrated, including system structure, system function and integrated system characters. The principle of fault warning system and the steps of fault diagnosis are descriped, emphasizing on the function and implementation of self-learning.
引文
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