Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions
Despite numerous approaches tailored for specific components and vehicle types, the automotive industry lacks a unified framework that integrates diverse methodologies while addressing scalability, adaptability, and efficiency across conventional (CVs), electric (EVs), and autonomous vehicles (AVs)....
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44533/ http://umpir.ump.edu.my/id/eprint/44533/1/Advances%20in%20intelligent%20vehicular%20health%20monitoring%20and%20fault%20diagnosis.pdf |
| _version_ | 1848827122837094400 |
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| author | Hossain, Md Naeem Rahman, Md Mustafizur D., Ramasamy |
| author_facet | Hossain, Md Naeem Rahman, Md Mustafizur D., Ramasamy |
| author_sort | Hossain, Md Naeem |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Despite numerous approaches tailored for specific components and vehicle types, the automotive industry lacks a unified framework that integrates diverse methodologies while addressing scalability, adaptability, and efficiency across conventional (CVs), electric (EVs), and autonomous vehicles (AVs). This study provides a taxonomic classification of vehicle health monitoring (VHM) and fault diagnosis techniques, highlighting the transformative role of advanced sensors, IoT integration, artificial intelligence (AI), and multi-sensor fusion. Through comparative evaluation of model-based, signal-based, and data-driven approaches, we identify their strengths, limitations, and optimal applications in different vehicular contexts supported by quantitative performance metrics. Key findings demonstrate that AI-driven fault diagnosis in CVs achieves up to 95 % accuracy in early mechanical failure detection, reducing maintenance costs and preventing catastrophic failures. Advanced battery monitoring techniques for EVs improve energy efficiency (15–20 %) and extend battery lifespan (10–15 %), addressing critical range anxiety and sustainability concerns. In AVs, sensor fusion and AI-based prediction achieve 99% reliability in real-time decision-making, enhancing operational safety and passenger confidence. We propose a novel conceptual framework integrating sensors, IoT, AI, and big data analytics to enhance VHM capabilities across all vehicle types. This framework addresses challenges in heterogeneous data integration, efficiency, and real-time processing in dynamic automotive environments. Future research addresses cybersecurity vulnerabilities, optimising AI models for edge computing deployments, developing AI techniques for transparent diagnostics, and leveraging vehicle-to-vehicle communication protocols to improve fault detection accuracy. This study contributes to the reactive to predictive maintenance strategies, potentially transforming maintenance practices across the transportation sector while enhancing vehicle reliability, safety, and sustainability. |
| first_indexed | 2025-11-15T03:55:42Z |
| format | Article |
| id | ump-44533 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:55:42Z |
| publishDate | 2025 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-445332025-05-09T04:26:45Z http://umpir.ump.edu.my/id/eprint/44533/ Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions Hossain, Md Naeem Rahman, Md Mustafizur D., Ramasamy TJ Mechanical engineering and machinery Despite numerous approaches tailored for specific components and vehicle types, the automotive industry lacks a unified framework that integrates diverse methodologies while addressing scalability, adaptability, and efficiency across conventional (CVs), electric (EVs), and autonomous vehicles (AVs). This study provides a taxonomic classification of vehicle health monitoring (VHM) and fault diagnosis techniques, highlighting the transformative role of advanced sensors, IoT integration, artificial intelligence (AI), and multi-sensor fusion. Through comparative evaluation of model-based, signal-based, and data-driven approaches, we identify their strengths, limitations, and optimal applications in different vehicular contexts supported by quantitative performance metrics. Key findings demonstrate that AI-driven fault diagnosis in CVs achieves up to 95 % accuracy in early mechanical failure detection, reducing maintenance costs and preventing catastrophic failures. Advanced battery monitoring techniques for EVs improve energy efficiency (15–20 %) and extend battery lifespan (10–15 %), addressing critical range anxiety and sustainability concerns. In AVs, sensor fusion and AI-based prediction achieve 99% reliability in real-time decision-making, enhancing operational safety and passenger confidence. We propose a novel conceptual framework integrating sensors, IoT, AI, and big data analytics to enhance VHM capabilities across all vehicle types. This framework addresses challenges in heterogeneous data integration, efficiency, and real-time processing in dynamic automotive environments. Future research addresses cybersecurity vulnerabilities, optimising AI models for edge computing deployments, developing AI techniques for transparent diagnostics, and leveraging vehicle-to-vehicle communication protocols to improve fault detection accuracy. This study contributes to the reactive to predictive maintenance strategies, potentially transforming maintenance practices across the transportation sector while enhancing vehicle reliability, safety, and sustainability. Elsevier Ltd 2025 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/44533/1/Advances%20in%20intelligent%20vehicular%20health%20monitoring%20and%20fault%20diagnosis.pdf Hossain, Md Naeem and Rahman, Md Mustafizur and D., Ramasamy (2025) Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions. Measurement, 253 (117618). pp. 1-22. ISSN 0263-2241. (Published) https://doi.org/10.1016/j.measurement.2025.117618 https://doi.org/10.1016/j.measurement.2025.117618 |
| spellingShingle | TJ Mechanical engineering and machinery Hossain, Md Naeem Rahman, Md Mustafizur D., Ramasamy Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions |
| title | Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions |
| title_full | Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions |
| title_fullStr | Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions |
| title_full_unstemmed | Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions |
| title_short | Advances in intelligent vehicular health monitoring and fault diagnosis: Techniques, technologies, and future directions |
| title_sort | advances in intelligent vehicular health monitoring and fault diagnosis: techniques, technologies, and future directions |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/44533/ http://umpir.ump.edu.my/id/eprint/44533/ http://umpir.ump.edu.my/id/eprint/44533/ http://umpir.ump.edu.my/id/eprint/44533/1/Advances%20in%20intelligent%20vehicular%20health%20monitoring%20and%20fault%20diagnosis.pdf |