AI-Driven Predictive Maintenance Framework for Intelligent Vehicle Health Monitoring

Year : 2026 | Volume : 13 | Issue : 01 | Page : 1 17
    By

    Chandani S. Bartakke,

  • Patil Sarang Maruti,

  • Mukesh Kumar Gupta,

  • Amit Tiwari,

  1. Research Scholar, Department of Computer Science and Engineering, Suresh Gyan Vihar University, Rajasthan, India
  2. Professor, Department of Computer Science and Engineering, Amity University, Maharashtra, India
  3. Professor, Department of Electrical Engineering, Suresh Gyan Vihar University, Rajasthan, India
  4. Assistant Professor, Department of Mechancial Engineering, Suresh Gyan Vihar University, Rajasthan, India

Abstract

The accelerated development of smart and connected car systems made the necessity to find the accurate and real-time predictive maintenance solutions which would minimize the number of unexpected failures as well as increase the cars on-road safety. The current paper proposes an artificial intelligence-based hybrid predictive maintenance system that combines Long Short-Memory (LSTM) networks and the XGBoost predictor to provide a potent vehicle fault diagnosis, Remaining Useful Life (RUL) prediction, and automatic maintenance warning system. The suggested approach involves the usage of the multisensor vehicle data, such as engine temperature, battery voltage, vibration, and electrical measurements to simulate the degradation trends and detect the early failure signs. As the results of the experiment prove, the hybrid model performs significantly better than the traditional machine learning methods, including Logistic Regression (LR), Random Forest (RF), and Gradient Boosting (XGBoost). Using the system, prediction accuracy of the fault-type is as follows: 97.3%, precision 97.0%, recall 97.1%, and F1-score 97.0%. To estimate RUL, Hybrid LSTM -XGBoost model is the least erroneous with a MAE of 8.3 hours and an RMSE of 12.8 hours which means that it is highly reliable in estimating maintenance. The analysis of confusion matrix and ROC curve de facto substantiate high discriminative ability of different types of faults with AUC. The performance analysis in terms of inference reveals that the prediction time of the model is still appropriate in the areas of real-time implementation within the edge-based vehicle health monitoring system. The results confirm that the suggested hybrid predictive maintenance structure is beneficial in the sense that it improves the quality of diagnostic results, reliability, and gives interpretable results that can be used to make proactive decisions. This work has advanced to the intelligent and data-driven maintenance ecosystem to enhance vehicle safety, minimize downtime, and reduce costs of operation.

Keywords: Predictive Maintenance, Vehicle Health Monitoring, Machine Learning Algorithms, Fault Diagnosis, Remaining Useful Life Prediction

[This article belongs to Trends in Machine design ]

How to cite this article:
Chandani S. Bartakke, Patil Sarang Maruti, Mukesh Kumar Gupta, Amit Tiwari. AI-Driven Predictive Maintenance Framework for Intelligent Vehicle Health Monitoring. Trends in Machine design. 2026; 13(01):1-17.
How to cite this URL:
Chandani S. Bartakke, Patil Sarang Maruti, Mukesh Kumar Gupta, Amit Tiwari. AI-Driven Predictive Maintenance Framework for Intelligent Vehicle Health Monitoring. Trends in Machine design. 2026; 13(01):1-17. Available from: https://journals.stmjournals.com/tmd/article=2026/view=239464


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Regular Issue Subscription Original Research
Volume 13
Issue 01
Received 11/02/2026
Accepted 13/02/2026
Published 25/02/2026
Publication Time 14 Days
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