IoT-Enabled predictive maintenance for sustainable transportation fleets
This study explores the use of Internet of Things (IoT) based predictive maintenance techniques for sustainable transportation fleets. It utilizes various datasets to enhance operational efficiency and reduce environmental consequences. An examination of the fleet data uncovers interesting findings:...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01189.pdf |
| _version_ | 1797248857004834816 |
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| author | Kansal Lavish Ediga Poornima |
| author_facet | Kansal Lavish Ediga Poornima |
| author_sort | Kansal Lavish |
| collection | DOAJ |
| description | This study explores the use of Internet of Things (IoT) based predictive maintenance techniques for sustainable transportation fleets. It utilizes various datasets to enhance operational efficiency and reduce environmental consequences. An examination of the fleet data uncovers interesting findings: the average mileage of the fleet is about 28,400 miles, indicating that different vehicles have been used to different extents. Notably, vehicle 002 stands out with the greatest mileage of 32,000 miles. Varying sensor measurements reveal discrepancies in tire pressure, brake pad thickness, and oil levels, suggesting different patterns of wear across the fleet. The historical maintenance data highlight the differences in maintenance intervals among automobiles. Based on predictive maintenance analysis, it is projected that vehicle 001 will need its next oil change after covering 27,000 miles, which is an increase of 2,000 miles compared to its last service. Percentage change study demonstrates the ever-changing nature of maintenance needs, highlighting the need of customized maintenance interventions that are specifically designed for each vehicle's unique characteristics. The combination of these discoveries clarifies the potential of IoT-enabled predictive maintenance in customizing tailored maintenance plans, increasing fleet efficiency, and reducing environmental impact. This research offers practical insights for adopting proactive maintenance techniques, promoting sustainability, and improving operational efficiency in transportation fleets. |
| first_indexed | 2024-04-24T20:21:15Z |
| format | Article |
| id | doaj.art-04cde9d3b431471184a0eededcde3b30 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:15Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-04cde9d3b431471184a0eededcde3b302024-03-22T08:05:26ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920118910.1051/matecconf/202439201189matecconf_icmed2024_01189IoT-Enabled predictive maintenance for sustainable transportation fleetsKansal Lavish0Ediga Poornima1Lovely Professional UniversityDepartment of AIMLE, GRIETThis study explores the use of Internet of Things (IoT) based predictive maintenance techniques for sustainable transportation fleets. It utilizes various datasets to enhance operational efficiency and reduce environmental consequences. An examination of the fleet data uncovers interesting findings: the average mileage of the fleet is about 28,400 miles, indicating that different vehicles have been used to different extents. Notably, vehicle 002 stands out with the greatest mileage of 32,000 miles. Varying sensor measurements reveal discrepancies in tire pressure, brake pad thickness, and oil levels, suggesting different patterns of wear across the fleet. The historical maintenance data highlight the differences in maintenance intervals among automobiles. Based on predictive maintenance analysis, it is projected that vehicle 001 will need its next oil change after covering 27,000 miles, which is an increase of 2,000 miles compared to its last service. Percentage change study demonstrates the ever-changing nature of maintenance needs, highlighting the need of customized maintenance interventions that are specifically designed for each vehicle's unique characteristics. The combination of these discoveries clarifies the potential of IoT-enabled predictive maintenance in customizing tailored maintenance plans, increasing fleet efficiency, and reducing environmental impact. This research offers practical insights for adopting proactive maintenance techniques, promoting sustainability, and improving operational efficiency in transportation fleets.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01189.pdfiotpredictive maintenancesustainable transportationfleet managementoperational efficiency |
| spellingShingle | Kansal Lavish Ediga Poornima IoT-Enabled predictive maintenance for sustainable transportation fleets MATEC Web of Conferences iot predictive maintenance sustainable transportation fleet management operational efficiency |
| title | IoT-Enabled predictive maintenance for sustainable transportation fleets |
| title_full | IoT-Enabled predictive maintenance for sustainable transportation fleets |
| title_fullStr | IoT-Enabled predictive maintenance for sustainable transportation fleets |
| title_full_unstemmed | IoT-Enabled predictive maintenance for sustainable transportation fleets |
| title_short | IoT-Enabled predictive maintenance for sustainable transportation fleets |
| title_sort | iot enabled predictive maintenance for sustainable transportation fleets |
| topic | iot predictive maintenance sustainable transportation fleet management operational efficiency |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01189.pdf |
| work_keys_str_mv | AT kansallavish iotenabledpredictivemaintenanceforsustainabletransportationfleets AT edigapoornima iotenabledpredictivemaintenanceforsustainabletransportationfleets |