Abstract:

Brakes are responsible for the stability of the vehicle. Brake failure is one of the key elements where more attention is required. Normally, a brake system failure is not an instantaneous process. It is caused by faults due to reasons like wear, mechanical fade, and oil leak, which started long before the failure progresses. Hence, it is essential to build a model that can recognize the condition from the signal. Condition monitoring is one such supervision approach, which continuously monitors the system and gives characteristics data. These data can be analysed and the condition of the component can be extracted using a machine learning approach. This study focuses on one such machine learning approach using the vibration characteristics of the brake system. The machine learning approach was carried out using feature extraction and feature classification. The statistical information extracted from the vibration signals under various fault conditions were used as features. The features were classified using machine learning algorithms, namely, Simple logistics, Logit boost and Multinominal Regression. Results were compared and discussed. The Logit boost algorithm, which produced 98.91 % classification accuracy, has been suggested as an effective approach for the brake fault diagnosis study.