Towards Wayside Wheel Flat Detection and Classification Based on Psychoacoustic Quantities

Abstract

Somewhat similar to the acoustic problems that were uncovered with the replacement of combustion engines with electric motors in cars, the retrofitting of all DB Cargo freight wagons with low-noise brakes caused other disturbing noise emissions to become more prominent. Among these, the periodic beating sounds originating from flattened wheel treads attract most attention. As there is no sufficiently validated solution for wayside wheel flat detection which meets the requirements of precise detection, focus on acoustic relevance and economic feasibility, the German Federal Environmental Agency has initiated a research project to determine an acoustic maintenance criterion for flat spots. In this contribution, we present the results of psychoacoustic analyses performed on a dataset comprising audio recordings of 200 cargo train passings. Regions with perceptible flat spots are labelled by trained ears. Common acoustic and psychoacoustic quantities are calculated for signal windows of classes "flat spot" and "no flat spot" and statistical analyses are performed to find quantities which qualify best for a separation of both classes. The effects of window size and sample rate are examined. Based on the results of listening tests, an acoustic classification criterion for the annoyance of wheel flats is proposed.