Nowadays, there are many methods to detect and diagnose defects in mechanical components during operation. The newest methods that can be found in the literature are based on intelligent classification systems and evaluation of patterns to obtain a diagnosis; however, there is not any standard method to assess features. Wavelet packet transform allows to obtain interesting patterns for evaluating the condition of rotating elements. To perform this calculation, it is necessary to select a series of parameters that affect the resulting pattern. These parameters are the decomposition level and the mother wavelet function. A detailed methodology for the selection of the mother wavelet is proposed, which is the aim of this work, to obtain the most suitable patterns in the diagnostic task. This proposed methodology is applied to data obtained from a rotating shaft with a crack located at the change of section. These signals were measured at low rotation frequency (below the critical rotation frequency) and without eccentricity, where detection becomes more complex.

目前，有多种方法可以检测和诊断机械部件在运行过程中的缺陷。文献中可以找到的最新方法是基于智能分类系统和模式评估以获得诊断；但是，没有任何标准方法来评估特征。小波包变换允许获得有趣的模式来评估旋转元素的条件。要执行此计算，必须选择影响结果模式的一系列参数。这些参数是分解级别和母小波函数。提出了一种选择母小波的详细方法，这是这项工作的目的，以获得诊断任务中最合适的模式。该方法适用于从具有位于截面变化处的裂纹的旋转轴获得的数据。这些信号是在低旋转频率（低于临界旋转频率）且没有偏心的情况下测量的，此时检测变得更加复杂。