Acoustic emission monitoring of wheel sets on moving trains

Abstract Recent work in acoustic emission monitoring of wheel sets on moving trains and trams using AE sensors mounted on the rails, has verified the great potential of the method in detecting geometric defects such as flat surfaces on wheel circumference. To achieve optimum results, multi-dimensional AE analysis has been performed, combining Time Driven Data (TDD), Hit Driven Data (HDD) and long waveform streams acquired simultaneously. High-frequency AE sensors proved to be adequately sensitive to flat defects, and the received AE signal level fluctuations were in agreement with the single defect rotational frequency. Combined analysis of all available types of AE data in the time and frequency domain with specialized software reveals the full potential, of the method in providing high accuracy defect detection. The present paper presents the results of AE measurements performed during train passes, with different speeds and directions, where wagons without any wheel defects and ones containing known artificial flats were moving along the rails. AE features, like signal amplitude, duration, energy, and average signal level. of AE signals are being analyzed and compared. Digital signal processing, post-test AE feature extraction and time measurements performed on acquired waveform streams that contain the total continuous acoustic emission during each pass for verification. The results clearly show repeatable indications on trains having defected wheels.