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The economic efficiency and competitiveness of environment-friendly rail transportation depends on safety, availability and maintenance of single highly loaded structure components. Until now these components have been changed in fixed maintenance intervals irrespective of any usage related conditions. With the knowledge and evaluation of the component conditions, life cycle costs can be reduced by means of optimized maintenance and/or “fit for purpose” design. For example, rail-bound vehicle wheel sets are among the most highly stressed travelling gear components of the bogie. if such a component fails, a serious accident may occur. For this reason, a health monitoring system based on the interpretation of ultrasonic sound signatures has been developed. First, the ultrasonic waves generated by an artificial defect on the outer wheel tread of a railroad wheel towards an acoustic sensor, placed inside the hollow shaft of the railroad axis were simulated with a EFIT (Elastodynamic Finite Integration Technique). The results achieved proved that relevant signals can be found in a frequency range up to 300 kHz.
Based on this a diagnostic unit was designed and built for application under rotation conditions, which consists of a piezo-electric sensor, primary electronics, an analog-to-digital converter, a digital signal processor, a trigger unit, and a telemetric transmitter. This diagnostic unit was integrated in the hollow shaft of a railroad wheel axis, a component of a special laboratory test rig. Algorithms which allow for the rotation-synchronized processing of acoustic signals were implemented into the rotating diagnostic unit. After successfully completing a campaign for this test rig, a second test was performed inside the wheel/railroad simulation test rig of the Deutsche Bahn AG under railroad-like conditions. The data generated inside the hollow shaft of the railroad wheel axis by the diagnostic unit were telemetrically transmitted to an industrial computer. The detection of artificial defects of different sizes is shown in correlation with theoretical assumptions.
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