Abstract

The novelty of this paper revolves around monitoring the condition of nitrogen-filled tires through the fusion of features and utilization of weightless neural networks. A tire pressure monitoring system (TPMS) plays a crucial role in ensuring vehicle safety and comfort. Reckless driving, poor road conditions, continual operation, higher road friction and excessive load are certain factors that can degrade the longevity of tires. Such conditions can result in instantaneous fault attacks in tires raising a concern for safety and comfort. To apply instantaneous and accurate fault diagnosis, the present study leverages machine learning techniques through the integration of an adaptive and robust algorithm, namely, the Wilkes, Stonham and Aleksander Recognition Device (WiSARD) classifier. The experiment uses three types of features namely, statistical, histogram and autoregressive moving average (ARMA) features. The J48 decision tree algorithm was used to pinpoint the key attributes crucial for classification. Following this, the identified attributes were segregated into training and testing datasets, facilitating the evaluation of the WiSARD classifier. Hyperparameter tuning was carried out to achieve optimal value for maximizing classification accuracy and minimizing computational time. Among the features considered, ARMA features delivered the best test set accuracy of about 96.18%.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.