Acoustic monitoring of rail faults in the German railway network

The early detection of rail surface defects such as squats, poor welds, or wheel burns is important to prevent further rail deterioration. In this paper, a methodology for acoustic monitoring of squats in the German railway network is proposed based on the measurement of axle box acceleration (ABA) on the DB noise measurement car (SMW) and the previously developed numerical model WERAN for wheel/rail interaction. Specific characteristics of squats in the ABA signals are determined with the model and verified by pass-by measurements combined with direct geometry measurements of the squats. Based on these re- sults, a logistic regression classifier is devised for the detection of squats in the measured ABA signals of the SMW. Trained with simulated and measured data, the classifier identifies all of the known severe squats and 87% of the known light squats in the measured test data

Statistical analysis of curve squeal based on long-term onboard noise measurements

Curve squeal with large magnitude tonal components in the frequency range up to 10 kHz is a cause of annoyance without any satisfying solution. This might be partly due to the gaps in the current understanding of the phenomenon within the research community (e.g. the open question whether the fundamental excitation mechanism is due to “falling friction” or “modal coupling”). Rail-bound traffic is expected to become a backbone in the future sustainable public transportation system. This makes it urgent to increase the state of knowledge in order to develop effective mitigation measures against the problem. Noise recorded by an onboard monitoring system during one year of traffic on the Stockholm metro is studied. The influence of selected variables on the generation of curve squeal is investigated in a statistical assessment. The influence of curve radius on curve squeal probability is estimated by calculating the quotient of squealing samples with respect to the total number of samples captured in circular curve sections. Vehicle speed (operative conditions) is modelled by the introduction of a classification representing different speed profiles (e.g. constant, linear acceleration or deacceleration, etc.). Environmental conditions are accounted for by using humidity and air temperature as predictor variables. A general trend of increased probability of curve squeal for decreasing curve radius is observed. Several subsequent regression analyses could not find a consistent influence of air temperature and humidity on the occurrence of curve squeal. Moreover, preliminary results indicate the existence of a vehicle speed for which a curve is particularly prone to generate squeal noise