Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2310979
Monitoring the condition of nitrogen-filled tires using weightless neural networks
Avantika Rattan
; School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India
S. Naveen Venkatesh
; School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India
V. Sugumaran
; School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India
*
P. S. Anoop
; School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, India
* Dopisni autor.
Sažetak
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%.
Ključne riječi
Tire pressure monitoring system (TPMS); machine learning; Wilkes, Stonham and Aleksander Recognition Device (WiSARD) classifier; fault diagnosis; hyperparameter
Hrčak ID:
323045
URI
Datum izdavanja:
11.2.2024.
Posjeta: 0 *