The following paper presents the methodology of monitoring the state of the ratchet mechanisms in the presence of noise. The fault detection is based on the acoustic analysis of the signals generated by the revolving mechanism. Decision is made using machine learning methods, which accuracy is compared. The object of the analysis is the ratchet mechanism installed in the actual BMX-type bicycle. It was shown that the noise suppression approach is suitable for the applied diagnostic framework, leading to the high accuracy in detecting catastrophic faults, such as breaking the tooth inside the mechanism.

19^th IMEKO TC10 Conference "Measurement for Diagnostics, Optimisation and Control to Support Sustainability and Resilience"