【作者】 魏巍；

【导师】 陈长征；

【作者基本信息】 沈阳工业大学 ， 机械工程， 2018， 硕士

【摘要】 近几年,由于全球变暖、雾霾等环境问题的出现,提高了人们对于环境的保护意识,所以风能作为最成熟的可再生的清洁能源正逐渐的取代煤炭、石油等传统的能源技术。随着风力发电机的尺寸正逐年增加,且服役在风沙、雨雪等极端环境下,承受着多变载荷的作用,使得风力机的主要传动部件的性能会发生严重的退化,而主轴承作为风力机的关键部件,其一旦发生故障,造成设备的停机,就会造成巨大的经济损失,所以能够及时的发现风力机主轴承早期的故障发生点并且准确地预测到未来的退化状态,并在恰当的时间对主轴承进行维修,对于保证风力发电机的正常运行具有非常重要的意义。因此,本文从振动信号的降噪预处理、故障特征向量的提取、特征向量衰退性能指标的建立、预测模型的建立及预测这四个方面进行研究,其主要研究内容包括以下几个方面:首先,针对风力发电机运行在复杂多变的极端环境下,采集到的振动信号具有非平稳、非线性的特点,并且掺杂许多噪声信号。为了滤除噪声信号,选择恰当的小波基先进行小波包分解,再根据计算出最优小波包树进行信号的重构,实现了对原始信号的降噪处理,提高了信噪比,为准确地提取故障特征向量提供了有利的保证。其次,针对传统的局部均值分解法(Local mean decomposition,LMD)也存在微弱的端点效应问题,提出了改进的LMD提取故障特征向量的方法。对重构的信号进行LMD分解,再计算出乘积函数(Product function,PF)分量与重构后信号的互相关系数和峭度值,剔除虚假分量同时增强故障信号幅值,然后对真实的PF分量进行包络谱分析,提取故障特征向量。实验结果表明,该方法有效地提取了早期的故障特征向量,并成功地诊断出滚动轴承的故障类型。最后,针对风力发电机主轴承的退化状态预测的问题,提出了基于改进的LMD与灰色模型(Grey model,GM)相结合的预测方法,将全寿命周期各阶段最能反映故障特征频率的PF向量的峭度值和RMS作为衰退性能指标,训练GM预测模型,用训练好的GM模型预测未来一段时间衰退性能指标的变化趋势,根据这两个衰退性能指标的变化曲线判断退化状态。通过风力机主轴承全寿命周期的实验数据结果表明,该方法预测出的两个衰退性能指标的变化趋势能够成功地判断出风力机主轴承未来运行状态的退化趋势。更多还原

【Abstract】 In recent years,due to the emergence of environmental issues such as global warming and haze,people’s awareness of environmental protection has been raised.Therefore,wind energy,as the most mature and renewable clean energy,is gradually replacing traditional energy technologies such as coal and petroleum.As the size of wind turbine is increasing year by year,and the service is subjected to variable loads in extreme environments such as wind,sand,rain,and snow,the performance of the main transmission components of wind turbine will be seriously degraded.Moreover,the main bearing is used as a key component of wind turbine,once it fails,which will cause equipment downtime and bring huge economic losses.Therefore,it is possible to timely detect the early failure point of wind turbine main bearing and accurately predict the future degradation state,and then the maintenance of the main bearing at the proper time has very important significance for ensuring the normal operation of wind turbine.Therefore,this paper studies the four aspects of de-nosing preprocessing of vibration signal,the extraction of fault eigenvector,the establishment of the eigenvector regression performance indexes,the establishment and prediction of prediction model.The main research content includes the following aspects:Firstly,aiming at the complicated and changeable extreme environment of wind turbine,the vibration signal is non-stationary and non-linear,and is mixed with many noise signals.In order to filter out the noise signal,the original fault signal is used to wavelet packet de-composition with appropriate wavelet base and reconstructed according to the calculated optimal wavelet packet tree to realize de-noising for the original signal and improve the signal-noise ratio,which can provide a good guarantee to extract the fault eigenvector of the vibration signal accurately.Secondly,aiming at weak endpoint effect problem of the traditional local mean decomposition,a fault feature extraction method is proposed based on improved LMD.The reconstructed signal is de-composed by using LMD method,and then the correlation coefficients between product function components and the reconstructed signal and kurtosis of PF components are calculated in order to eliminate the false component and enhance the amplitude of fault signal.Then envelope spectrum analysis of real PF component is carried out,and the fault feature of fault signal is extracted.The experimental results show that the improved LMD method can effectively extract the early fault eigenvector and successfully diagnose the fault type of the roller bearing.Finally,aiming at the problem of degraded state prediction of wind turbine main bearing,a prediction method is proposed based on improved LMD and gray model.The kurtosis value and RMS of the PF components that can best reflect the frequency of fault feature in each stage of the full life cycle are used as regression performance indexes to train the GM prediction model.Then,the trained GM model is used to predict the trend of regression performance indexes in the future period of time,and the degradation state is determined according to the curve of the two regression performance indexes.Through the experimental data of full life cycle of wind turbine main bearing,the results show that the trend of the two regression performance indexes predicted by this method can successfully determine the deteriorating trend of the future running state of wind turbine main bearing.更多还原