Mechanical Engineering Department, Avenida de la Universidad 30, 28982 Madrid, Spain.
Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Via G. Amendola 2, 42124 Reggio Emilia, Italy.
Sensors (Basel). 2023 Jul 4;23(13):6143. doi: 10.3390/s23136143.
The detection of cracks in rotating machinery is an unresolved issue today. In this work, a methodology for condition monitoring of railway axles is presented, based on crack detection by means of the automatic selection of patterns from the vibration signal measurement. The time waveforms were processed using the Wavelet Packet Transform, and appropriate alarm values for diagnosis were calculated automatically using non-supervised learning techniques based on Change Point Analysis algorithms. The validation was performed using vibration signals obtained during fatigue tests of two identical railway axle specimens, one of which cracked during the test while the other did not. During the test in which the axle cracked, the results show trend changes in the energy of the vibration signal associated with theoretical defect frequencies, which were particularly evident in the direction of vibration that was parallel to the track. These results are contrasted with those obtained during the test in which the fatigue limit was not exceeded, and the test therefore ended with the axle intact, verifying that the effects that were related to the crack did not appear in this case. With the results obtained, an adjusted alarm value for a condition monitoring process was established.
今天，旋转机械裂纹的检测仍然是一个悬而未决的问题。在这项工作中，提出了一种基于通过自动从振动信号测量中选择模式来检测裂纹的铁路车轴状态监测方法。时间波形使用小波包变换进行处理，并使用基于变化点分析算法的无监督学习技术自动计算适当的诊断报警值。使用从两个相同的铁路车轴样本的疲劳试验中获得的振动信号进行了验证，其中一个在试验过程中发生了裂纹，而另一个则没有。在车轴发生裂纹的试验中，结果表明与理论缺陷频率相关的振动信号能量发生了趋势变化，在与轨道平行的振动方向上尤其明显。这些结果与未超过疲劳极限的试验进行了对比，因此试验结束时车轴保持完好，验证了在这种情况下与裂纹相关的影响并未出现。根据所得到的结果，为状态监测过程建立了一个调整后的报警值。
