Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers
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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.13057 |
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| author | Arpit Pandey Sridharan Naveen Venkatesh Prabhakaranpillai Sreelatha Anoop B. R. Manju Vaithiyanathan Sugumaran |
| author_facet | Arpit Pandey Sridharan Naveen Venkatesh Prabhakaranpillai Sreelatha Anoop B. R. Manju Vaithiyanathan Sugumaran |
| author_sort | Arpit Pandey |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-65266d4540884f3c877b2bcbddccc76e |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-65266d4540884f3c877b2bcbddccc76e2025-01-31T00:22:49ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13057Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy ClassifiersArpit Pandey0Sridharan Naveen Venkatesh1Prabhakaranpillai Sreelatha Anoop2B. R. Manju3Vaithiyanathan Sugumaran4School of Mechanical Engineering (SMEC) Vellore Institute of Technology Chennai IndiaDivision of Operation and Maintenance Engineering Luleå Tekniska Universitet Luleå SwedenDepartment of Mechanical Engineering Providence College of Engineering Alappuzha IndiaDepartment of Mathematics, School of Arts and Sciences Amrita Vishwa Vidyapeetham Amritapuri Campus Kollam IndiaSchool of Mechanical Engineering (SMEC) Vellore Institute of Technology Chennai IndiaABSTRACT 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.https://doi.org/10.1002/eng2.13057feature fusionlazy‐based classifierslocally weighted learning (lwl)tire pressure monitoring system (TPMS)vibration analysis |
| spellingShingle | Arpit Pandey Sridharan Naveen Venkatesh Prabhakaranpillai Sreelatha Anoop B. R. Manju Vaithiyanathan Sugumaran Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers Engineering Reports feature fusion lazy‐based classifiers locally weighted learning (lwl) tire pressure monitoring system (TPMS) vibration analysis |
| title | Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers |
| title_full | Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers |
| title_fullStr | Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers |
| title_full_unstemmed | Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers |
| title_short | Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers |
| title_sort | tire pressure monitoring system using feature fusion and family of lazy classifiers |
| topic | feature fusion lazy‐based classifiers locally weighted learning (lwl) tire pressure monitoring system (TPMS) vibration analysis |
| url | https://doi.org/10.1002/eng2.13057 |
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