{"id":{"repo_id":"wlv","oai_identifier":"oai:wlv.openrepository.com:2436/626399"},"canonical_url":"https://search.dev.ndltd.org/etd/wlv/oai:wlv.openrepository.com:2436/626399","repository":{"repo_id":"wlv","name":"University of Wolverhampton","base_url":"https://wlv.openrepository.com/server/oai/request"},"display":{"title":"Big data toward vehicle health monitoring system: an engine health perspective","abstract":"The rapid growth of connected and sensor rich vehicles has produced unprecedented volumes of engine telemetry, posing critical challenges for traditional vehicle health monitoring system (VHMS). Existing systems struggle with high volume, high velocity, and heterogeneous data streams, limiting real-time fault detection, predictive maintenance, and operational reliability. Addressing these limitations requires a cohesive, multi-layered framework that unifies scalable data management, real-time analytics, and intelligent predictive modeling. This thesis leverages a VHMS dataset initially collected by (Rahman et al., 2022) and augmented from 3,003 to 200,000 records with 18 critical features, including vibration, Controller Area Network bus, combustion, lubrication, and thermal signals. Using this high dimensional, sequential, and heterogeneous data, this research developed an integrated end-to-end VHMS paradigm comprising four key contributions: (i) A conceptual big data framework that defined a taxonomy for data collection, preprocessing, storage, and analysis, ensuring modularity, traceability, and scalability across heterogeneous vehicle data sources. (ii) An optimized hybrid big data processing architecture that integrate hadoop for fault tolerant historical storage, Spark for in-memory analytics, and Kafka for real-time streaming. This framework dynamically switches between batch and streaming workloads, achieving 0.030s execution time, 1.67 × 10−7s per-row latency, and a 55.01% reduction in network load compared to standalone approaches (Chukwudi et al., 2025). (iii) A hybrid deep learning diagnostic Model, capable of modeling temporal dependencies and adapting across diverse vehicle types. This model attains 92.18% testing accuracy with a Receiver Operating Characteristic and Area Under the Curve (ROC AUC) of 0.965, outperforming conventional multilayer perception (MLP), long short-term memory (LSTM), and bidirectional gated recurrent units (BiGRU) architectures by 15–20% in both accuracy and generalization (Md Abdur Rahim et al., 2025). (iv) A real-time diagnostic model, combining Support Vector Machines, K-Neighbors, Gradient Boosting, and Decision Trees through a meta-learning layer. The ensemble achieved 94.7% accuracy and Area Under the Curve (AUC) of 0.9702, demonstrating superior robustness to sensor noise and overlapping engine states compared to single model approaches (Chukwudi et al., 2024; Rahman et al., 2022). Collectively, these contributions established a robust, interpretable, and adaptive VHMS paradigm capable of real-time diagnostics, predictive maintenance, and operational decision support. Comparative evaluation confirmed the improvements in accuracy, latency, scalability, and reliability over conventional VHMS. Furthermore, the framework provides a foundation for future studies in federated learning, edge-cloud deployment, and multimodal sensor fusion, offering scalable, low-cost, and inclusive solutions for modern transportation systems. This research advances both practical and scholarly understanding of intelligent VHMS, demonstrating that integrating data engineering, machine learning, and decision-oriented system design is critical for next-generation vehicle diagnostics.","abstract_html":"The rapid growth of connected and sensor rich vehicles has produced unprecedented volumes of engine telemetry, posing critical challenges for traditional vehicle health monitoring system (VHMS). Existing systems struggle with high volume, high velocity, and heterogeneous data streams, limiting real-time fault detection, predictive maintenance, and operational reliability. Addressing these limitations requires a cohesive, multi-layered framework that unifies scalable data management, real-time analytics, and intelligent predictive modeling. This thesis leverages a VHMS dataset initially collected by (Rahman et al., 2022) and augmented from 3,003 to 200,000 records with 18 critical features, including vibration, Controller Area Network bus, combustion, lubrication, and thermal signals. Using this high dimensional, sequential, and heterogeneous data, this research developed an integrated end-to-end VHMS paradigm comprising four key contributions: (i) A conceptual big data framework that defined a taxonomy for data collection, preprocessing, storage, and analysis, ensuring modularity, traceability, and scalability across heterogeneous vehicle data sources. (ii) An optimized hybrid big data processing architecture that integrate hadoop for fault tolerant historical storage, Spark for in-memory analytics, and Kafka for real-time streaming. This framework dynamically switches between batch and streaming workloads, achieving 0.030s execution time, 1.67 × 10−7s per-row latency, and a 55.01% reduction in network load compared to standalone approaches (Chukwudi et al., 2025). (iii) A hybrid deep learning diagnostic Model, capable of modeling temporal dependencies and adapting across diverse vehicle types. This model attains 92.18% testing accuracy with a Receiver Operating Characteristic and Area Under the Curve (ROC AUC) of 0.965, outperforming conventional multilayer perception (MLP), long short-term memory (LSTM), and bidirectional gated recurrent units (BiGRU) architectures by 15–20% in both accuracy and generalization (Md Abdur Rahim et al., 2025). (iv) A real-time diagnostic model, combining Support Vector Machines, K-Neighbors, Gradient Boosting, and Decision Trees through a meta-learning layer. The ensemble achieved 94.7% accuracy and Area Under the Curve (AUC) of 0.9702, demonstrating superior robustness to sensor noise and overlapping engine states compared to single model approaches (Chukwudi et al., 2024; Rahman et al., 2022). Collectively, these contributions established a robust, interpretable, and adaptive VHMS paradigm capable of real-time diagnostics, predictive maintenance, and operational decision support. Comparative evaluation confirmed the improvements in accuracy, latency, scalability, and reliability over conventional VHMS. Furthermore, the framework provides a foundation for future studies in federated learning, edge-cloud deployment, and multimodal sensor fusion, offering scalable, low-cost, and inclusive solutions for modern transportation systems. This research advances both practical and scholarly understanding of intelligent VHMS, demonstrating that integrating data engineering, machine learning, and decision-oriented system design is critical for next-generation vehicle diagnostics.","abstract_has_math":false,"creators":["Isinka, Chukwudi Joseph"],"institution":"University of Wolverhampton","degree_name":"PhD","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Rahman, Md Arafatur"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026","date_published":"2026","updated_at":"2026-07-24T06:10:04Z","subjects":["big data","engine health","vehicle monitoring","predictive maintenance","big data processing","real-time","stacked ensemble model","optimised hybrid big data processing","attentive mechanism"],"languages":[],"rights":[],"rights_urls":["https://wlv.openrepository.com/bitstreams/f4c6ea54-0d11-4d22-8cb6-6c613ac1ccc2/download"],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Rahman, Md Arafatur"]},{"key":"dc:creator","label":"Author","values":["Isinka, Chukwudi Joseph"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2026"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Wolverhampton"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://wlv.openrepository.com/handle/2436/626399"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["big data","engine health","vehicle monitoring","predictive maintenance","big data processing","real-time","stacked ensemble model","optimised hybrid big data processing","attentive mechanism"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["https://wlv.openrepository.com/bitstreams/f4c6ea54-0d11-4d22-8cb6-6c613ac1ccc2/download"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://wlv.openrepository.com/bitstreams/882c6f55-1182-493d-8bb7-d3a0cfa31eff/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The rapid growth of connected and sensor rich vehicles has produced unprecedented volumes of engine telemetry, posing critical challenges for traditional vehicle health monitoring system (VHMS). 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(ii) An optimized hybrid big data processing architecture that integrate hadoop for fault tolerant historical storage, Spark for in-memory analytics, and Kafka for real-time streaming. This framework dynamically switches between batch and streaming workloads, achieving 0.030s execution time, 1.67 × 10−7s per-row latency, and a 55.01% reduction in network load compared to standalone approaches (Chukwudi et al., 2025). (iii) A hybrid deep learning diagnostic Model, capable of modeling temporal dependencies and adapting across diverse vehicle types. This model attains 92.18% testing accuracy with a Receiver Operating Characteristic and Area Under the Curve (ROC AUC) of 0.965, outperforming conventional multilayer perception (MLP), long short-term memory (LSTM), and bidirectional gated recurrent units (BiGRU) architectures by 15–20% in both accuracy and generalization (Md Abdur Rahim et al., 2025). 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(ii) An optimized hybrid big data processing architecture that integrate hadoop for fault tolerant historical storage, Spark for in-memory analytics, and Kafka for real-time streaming. This framework dynamically switches between batch and streaming workloads, achieving 0.030s execution time, 1.67 × 10−7s per-row latency, and a 55.01% reduction in network load compared to standalone approaches (Chukwudi et al., 2025). (iii) A hybrid deep learning diagnostic Model, capable of modeling temporal dependencies and adapting across diverse vehicle types. This model attains 92.18% testing accuracy with a Receiver Operating Characteristic and Area Under the Curve (ROC AUC) of 0.965, outperforming conventional multilayer perception (MLP), long short-term memory (LSTM), and bidirectional gated recurrent units (BiGRU) architectures by 15–20% in both accuracy and generalization (Md Abdur Rahim et al., 2025). 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