A stacking ensemble classification model for determining the state of nitrogen-filled car tires
Tire pressure monitoring systems (TPMS) are essential for vehicle safety and performance as they help detect low tire pressure that impacts fuel efficiency, ride comfort, and overall safety. This study introduces a novel stacking ensemble model to improve the monitoring of nitrogen-filled pneumatic...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-03-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2024-0358 |
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| _version_ | 1849394633384132608 |
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| author | Shah Viraj Chetan Sridharan Naveen Venkatesh Vaithiyanathan Sugumaran Sreelatha Anoop Prabhakaranpillai Radha Manju Bhaskarapanicker |
| author_facet | Shah Viraj Chetan Sridharan Naveen Venkatesh Vaithiyanathan Sugumaran Sreelatha Anoop Prabhakaranpillai Radha Manju Bhaskarapanicker |
| author_sort | Shah Viraj Chetan |
| collection | DOAJ |
| description | Tire pressure monitoring systems (TPMS) are essential for vehicle safety and performance as they help detect low tire pressure that impacts fuel efficiency, ride comfort, and overall safety. This study introduces a novel stacking ensemble model to improve the monitoring of nitrogen-filled pneumatic tires. Vibration signals, captured under four conditions such as idle, highspeed, normal, and puncture, using low-cost MEMS accelerometers, are processed to derive autoregressive moving average (ARMA), histogram, and statistical features. The J48 decision tree is employed for feature selection, enhancing classifier accuracy. Experiments with various machine learning classifiers show that the stacking ensemble approach significantly improves classification performance for ARMA (93.75%) and histogram (85.42%) features, thereby achieving higher accuracy than individual classifiers. These findings demonstrate that stacking ensembles can enhance TPMS capabilities, offering a cost-effective and accurate solution for real-time tire pressure monitoring. This advancement contributes to automotive safety and maintenance by enabling more reliable and precise TPMS. |
| format | Article |
| id | doaj-art-d13ea8e90190419aa902ffb76b29c4d3 |
| institution | Kabale University |
| issn | 2191-026X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-d13ea8e90190419aa902ffb76b29c4d32025-08-20T03:39:56ZengDe GruyterJournal of Intelligent Systems2191-026X2025-03-01341035210910.1515/jisys-2024-0358A stacking ensemble classification model for determining the state of nitrogen-filled car tiresShah Viraj Chetan0Sridharan Naveen Venkatesh1Vaithiyanathan Sugumaran2Sreelatha Anoop Prabhakaranpillai3Radha Manju Bhaskarapanicker4School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, 600127, IndiaDivision of Operation and Maintenance Engineering, Luleå Tekniska Universitet, 97187, Luleå, SwedenSchool of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai, 600127, IndiaDepartment of Mechanical Engineering, Providence College of Engineering, Alappuzha, 689122, IndiaDepartment of Mathematics, School of Physical Sciences, Amrita Vishwa Vidyapeetham Amritapuri Campus, Kollam, 690525, IndiaTire pressure monitoring systems (TPMS) are essential for vehicle safety and performance as they help detect low tire pressure that impacts fuel efficiency, ride comfort, and overall safety. This study introduces a novel stacking ensemble model to improve the monitoring of nitrogen-filled pneumatic tires. Vibration signals, captured under four conditions such as idle, highspeed, normal, and puncture, using low-cost MEMS accelerometers, are processed to derive autoregressive moving average (ARMA), histogram, and statistical features. The J48 decision tree is employed for feature selection, enhancing classifier accuracy. Experiments with various machine learning classifiers show that the stacking ensemble approach significantly improves classification performance for ARMA (93.75%) and histogram (85.42%) features, thereby achieving higher accuracy than individual classifiers. These findings demonstrate that stacking ensembles can enhance TPMS capabilities, offering a cost-effective and accurate solution for real-time tire pressure monitoring. This advancement contributes to automotive safety and maintenance by enabling more reliable and precise TPMS.https://doi.org/10.1515/jisys-2024-0358stackingtpmsfeature extractionfeature selectionensemble methodology68 and 62 |
| spellingShingle | Shah Viraj Chetan Sridharan Naveen Venkatesh Vaithiyanathan Sugumaran Sreelatha Anoop Prabhakaranpillai Radha Manju Bhaskarapanicker A stacking ensemble classification model for determining the state of nitrogen-filled car tires Journal of Intelligent Systems stacking tpms feature extraction feature selection ensemble methodology 68 and 62 |
| title | A stacking ensemble classification model for determining the state of nitrogen-filled car tires |
| title_full | A stacking ensemble classification model for determining the state of nitrogen-filled car tires |
| title_fullStr | A stacking ensemble classification model for determining the state of nitrogen-filled car tires |
| title_full_unstemmed | A stacking ensemble classification model for determining the state of nitrogen-filled car tires |
| title_short | A stacking ensemble classification model for determining the state of nitrogen-filled car tires |
| title_sort | stacking ensemble classification model for determining the state of nitrogen filled car tires |
| topic | stacking tpms feature extraction feature selection ensemble methodology 68 and 62 |
| url | https://doi.org/10.1515/jisys-2024-0358 |
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