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Feasibility study of a rail vehicle damper fault detection by artificial neural networks
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Online publication date: 2023-01-27
Publication date: 2023-01-27
Eksploatacja i Niezawodność – Maintenance and Reliability 2023;25(1):5
  • Primary suspension damper failures affect vehicle dynamics.
  • Detection of damping reduction is based on the analysis of acceleration signals in frequency domain.
  • Artificial neural networks of different number of hidden layers were applied to accelerations’ PSDs.
  • ANNs training process was a difficult task, resulting in fault detection rate below 63%.
The aim of the study was to investigate rail vehicle dynamics under primary suspension dampers faults and explore possibility of its detection by means of artificial neural networks. For these purposes two types of analysis were carried out: preliminary analysis of 1 DOF rail vehicle model and a second one - a passenger coach benchmark model was tested in multibody simulation software - MSC.Adams with use of VI-Rail package. Acceleration signals obtained from the latter analysis served as an input data into the artificial neural network (ANN). ANNs of different number of hidden layers were capable of detecting faults for the trained suspension fault cases, however, achieved accuracy was below 63% at the best. These results can be considered satisfactory considering the complexity of dynamic phenomena occurring in the vibration system of a rail vehicle.