Publication

Abstract :

The tire pressure monitoring system (TPMS) is crucial for road safety, fuel efficiency, and vehicle performance. This study focuses on nitrogen-filled pneumatic tires due to their uniform pressure management and thermal stability advantages over air-filled tires. Using machine learning, the research analyzes TPMS data to enhance understanding of tire behavior and vehicle safety. It employs various feature extraction methods and lazy-based classifiers to analyze vibration signals collected under idle, high-speed, normal, and puncture conditions using MEMS accelerometers. The study examines autoregressive moving average (ARMA), histogram, and statistical features individually and in combinations (statistical-histogram, histogram-ARMA, statistical-ARMA, and statistical-histogram-ARMA) to improve predictive accuracy. By integrating these features, the study aims to optimize predictive modeling of TPMS. Empirically, the research achieved 97.92% accuracy using the local weighted learning (LWL) algorithm, demonstrating the effectiveness of combined statistical, histogram, and ARMA features in enhancing TPMS predictive capabilities.