Bustos Alejandro, Rubio Higinio, Castejón Cristina, García-Prada Juan Carlos
MAQLAB Research Group, Department of Mechanical Engineering, Universidad Carlos III de Madrid, Av. de la Universidad, 30, 28911 Leganes (Madrid), Spain.
Sensors (Basel). 2018 Mar 6;18(3):793. doi: 10.3390/s18030793.
An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.
高效维护是铁路运输系统中的关键考量因素，尤其是在高速列车中，以避免发生具有灾难性后果的事故。从这个意义上讲，拥有一种能够早期检测关键部件（如转向架机械部件）缺陷的方法，对于提高机车车辆的可用性和降低维护成本至关重要。这项工作的主要贡献在于提出了一种基于经典信号处理技术的方法，该方法提供了一组用于快速识别关键机械系统运行状态的参数。通过这种方法，利用可变输入对一个非常复杂的机械系统的振动行为进行表征，这将有助于检测机械部件中可能出现的变化。该方法应用于一列商业运营的实际高速列车，旨在研究列车（具体为转向架）在维护操作前后的振动行为。用这种方法获得的结果证明了新程序的有效性，并揭示了维护操作后所选固有模态函数（IMF）的频谱功率降低了15%至45%。
