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首页> 外文期刊>Mechanical systems and signal processing >Diagnostics of bearings in presence of strong operating conditions non-stationarity-A procedure of load-dependent features processing with application to wind turbine bearings
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Diagnostics of bearings in presence of strong operating conditions non-stationarity-A procedure of load-dependent features processing with application to wind turbine bearings

机译：在非平稳性强的工作条件下进行轴承诊断-应用于风力涡轮机轴承的负载相关特征处理程序

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Condition monitoring of bearings used in Wind Turbines (WT) is an important issue. In general, bearings diagnostics is a well recognized field of research; however, it is not the case for machines operating under non-stationary load. In the case of varying load/speed, vibration signal generated by rolling element bearings is affected by operation factors, and makes the diagnosis relatively difficult. These difficulties come from the variation of vibration-based diagnostic features caused mostly by load/speed variation (operation factors), low energy of sought-after features, and low signal-to-noise levels. Analysis of the signal from the main bearing is even more difficult due to a very low rotational speed of the main shaft. In the paper, a novel diagnostic approach is proposed for bearings used in wind turbines. As an input data we use parameters obtained from commercial diagnostic system (peak-to-peak and root mean square (RMS) of vibration acceleration, and generator power that is related to the operating conditions). The received data cover the period of several months. The method presented in the paper was triggered by two case studies, which will be presented here: first when the bearing has been replaced due to its failure and the new one has been installed, second when bearing in good condition has significantly changed its condition. Due to serious variability of the mentioned data, a decision making process on the condition of bearings is difficult. Application of classical statistical pattern recognition techniques for "bad condition" and "good condition" data is not sufficient because the probability distribution/density functions (pdf) of features overlap each other (for example probability distribution/density function of peak-to-peak feature for bad and good conditions). It was found that these data are strongly dependent on operating condition (generator power) variation, and there is a need to remove such dependency by suitable data presentation. To achieve it, load susceptibility characteristics (LSCh) presenting as feature - operating condition space has been used. Presented approach is based on an idea proposed earlier for planetary gearboxes, i.e. to analyse data for bad/good conditions in two dimensional space, feature - load/rotation speed. Here it has been proven experimentally for the first time that there are two types of susceptibility characteristics related to the type of a fault. The novelty of the paper also comes from an extension of previous study that is statistical processing of data (linear regression analysis) in moving window in the long time of a turbine operation is used for feature extraction. It is proposed here to use novel features for long term monitoring. It will be shown that parameters of regression analysis can be used as unvarying, and fault sensitive features for decision making.
机译：风力涡轮机（WT）中使用的轴承状态监测是一个重要的问题。通常，轴承诊断是一个公认的研究领域。但是，在非静态负载下运行的机器并非如此。在负载/速度变化的情况下，滚动轴承产生的振动信号会受到操作因素的影响，使诊断相对困难。这些困难来自于基于振动的诊断功能的变化，这些变化主要是由负载/速度变化（操作因素），低能量的需求特性以及低信噪比引起的。由于主轴的转速非常低，因此分析来自主轴承的信号更加困难。在本文中，提出了一种用于风力涡轮机轴承的新颖诊断方法。作为输入数据，我们使用从商业诊断系统获得的参数（振动加速度的峰均值和均方根（RMS），以及与运行条件相关的发电机功率）。收到的数据涵盖了几个月的时间。本文中介绍的方法是由两个案例研究触发的，将在此处介绍：第一，由于轴承故障而更换了轴承，并且安装了新轴承，第二，当状况良好的轴承已显着改变其状况时。由于上述数据的严重可变性，因此难以根据轴承情况进行决策。由于“特征”的概率分布/密度函数（pdf）相互重叠（例如峰-峰的概率分布/密度函数），因此将经典的统计模式识别技术应用于“不良条件”和“良好条件”数据是不够的的条件）。已经发现，这些数据强烈地取决于运行条件（发电机功率）的变化，并且需要通过适当的数据表示来消除这种依赖性。为了实现这一目标，已使用表现为特征-运行条件空间的负荷敏感性特征（LSCh）。提出的方法基于先前针对行星齿轮箱提出的想法，即分析二维空间中特征/负载/转速的不良/良好条件的数据。在此首次通过实验证明，与故障类型有关的磁化率特性有两种。本文的新颖性还源于先前研究的扩展，该研究是在涡轮运行的长时间内对移动窗口中数据的统计处理（线性回归分析）用于特征提取。在此建议使用新颖的功能进行长期监控。结果表明，回归分析的参数可以用作不变的，对决策敏感的故障特征。

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