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The vehicles usage is increasing day by day due to the recent, technological development in the automotive field. In a competitive global market in order to survive, the reliability needs to be ensured, through a proper monitoring system. Brake system is one such control component, in which much focus is very much essential. An efficient brake system should provide reliable and effective performance in order to ensure the safety. If it is not properly monitored, it may lead to a serious catastrophic effect such as accidents, brake down, etc. Hence, the brake system needs to be monitored continuously. In this study, an experimental investigation was carried out for monitoring the brake system using vibration signals. An experimental setup which resembles the brake system was fabricated. The vibration signals were acquired under various brake condition such as good and faulty. From the acquired vibration signals, the features were extracted using statistical and histogram feature extraction techniques and feature selection was carried out. The selected features were then classifieds using a Naive Bayes and Bayes Net a classifier. The classification accuracy of all the algorithms were compared for finding the best feature classifier model for monitoring the brake condition.

Real time condition monitoring of hydraulic brake system using naive bayes and bayes net algorithms