Effectiveness of Advanced Vibration Processing Techniques for Fault Detection in Heavy Duty Wheels

This paper assesses the application of different processing techniques on acceleration signals extracted from faulty heavy duty wheels. Heavy duty wheels are used in applications as automatic vehicles and are mainly composed of a polyurethane tread glued to a cast iron hub. The adhesive application between tread and hub is the most critical assembly phase, since it is completely made by an operator and a contamination of the link area may happen. Furthermore the presence of rust on the hub surface can contribute to worsen the adherence interface, reducing the operating life. Several wheels with different types of faults have been manufactured ad hoc with anomalies similar to the ones that can really be originated. Synchronous average is calculated over the wheel rotation in order to highlight the phenomena that have the wheel rotation as periodicity (e.g. the contact between defect and test bench drum). Successively, cyclostationary theory is applied to extract information from the frequency/order domain of the processed signals. Eventually, well-suited indicators/coefficients are applied to the processed signals, objectifying the anomaly presence and defining pass-fail reference values based on the nonstatistical Tukey’s method.