Condition Monitoring and Diagnostics in Heavy-Duty Wheels: A First Experimental Approach

This paper assesses and compares the effectiveness of different analysis techniques for fault detection and diagnostics in heavy-duty wheels by using vibro-acoustic data. Firstly, different defect types have been artificially created on the wheels, trying to replicate anomalies that could really happen within the manufacturing process. Hence, different sensors and test conditions have been tested in order to determine the set up that at the best highlights the anomalies of the wheels; moreover the Time Synchronous Average (TSA) has been computed to reduce measurement noise. Kurtosis statistical coefficient has been used to detect defect presence (condition monitoring step), whereas frequency analysis, time-frequency analysis and signal trend have been performed for identifying the type of defect (diagnosis step). Finally, the effectiveness and the limitations of the above-mentioned techniques and diagnostics procedures are compared and discussed in order to define a systematic control at the end of the production line.