Determination of the Condition of Railway Rolling Stock Using Automatic Classifiers

Efficient maintenance is paramount for rail transport systems to avoid catastrophic accidents. Therefore, a method that enables the early detection of defects in critical components is crucial for increasing the availability of rolling stock and reducing maintenance costs. This work's main contribution is the proposal of a methodology for analyzing vibration signals. The vibration signals, obtained from a bogie axle on a test bench, are decomposed into intrinsic functions, to which classical signal processing techniques are then applied. Finally, decision trees are employed to characterize the axle's state, yielding excellent results.

condition monitoring; decision trees; empirical mode decomposition; freight train; vibration analysis

Determination of the Condition of Railway Rolling Stock Using Automatic Classifiers Articles