Evaluating IIoT-Based Predictive Maintenance Techniques for Modern Automobiles: A Comparative Survey

Abstract

This survey paper provides a comprehensive review of predictive maintenance methodologies employed in the automotive industry. It explores the role of IIoT in data collection, transmission, and analysis, compares various predictive main- tenance techniques, and discusses their benefits, limitations, and future trends. The paper also presents a comparative study of different methodologies, includ- ing machine learning models, digital twins, stochastic methods, and fuzzy logic systems, to evaluate their effectiveness in real-world automotive applications. By offering a detailed assessment of existing research, this study aims to bridge the gap between theoretical advancements and practical implementations, providing valuable insights for researchers, industry professionals, and automotive manufacturers looking to enhance vehicle maintenance strategies through IIoT.

Introduction

Traditional automotive maintenance approaches—reactive and preventive—are being replaced by predictive maintenance, driven by the Industrial Internet of Things (IIoT). This method uses real-time sensor data, AI, and machine learning (ML) to detect faults before they occur, reducing downtime, improving safety, and lowering maintenance costs. Companies like Tesla, BMW, and Ford are already adopting this technology.

2. Background and Motivation

• Reactive maintenance leads to costly, unplanned failures.

• Preventive maintenance can cause unnecessary part replacements.

• Predictive maintenance offers:

  • Improved reliability and customer satisfaction

  • Cost efficiency and sustainability (by reducing waste and extending component life)

  • Better alignment with increasing vehicle complexity and user expectations

• However, existing literature lacks a comprehensive comparison of predictive maintenance methods, such as ML, digital twins, and stochastic models.

3. IIoT Architecture in Automotive Maintenance

Predictive maintenance relies on a multi-layer IIoT architecture:

• Perception Layer: In-vehicle sensors monitor parameters like temperature, pressure, vibration, and battery metrics.

• Network Layer: Data transmission via protocols like CAN, LIN, FlexRay and wireless tech (Wi-Fi, 5G).

• Data Processing Layer: Data is processed on the cloud or edge devices using ML or rule-based systems for pattern recognition and anomaly detection.

• Application Layer: Insights are delivered via dashboards or mobile apps, enabling actions like dynamic maintenance scheduling.

4. Predictive Maintenance Techniques

1. Machine Learning (ML): Used for predicting component lifespan, detecting anomalies, and fault classification. Methods include decision trees, SVMs, neural networks, etc.

2. Digital Twins: Real-time virtual replicas of vehicle systems that simulate behavior and predict failures.

3. Stochastic Modeling: Uses probability-based models (e.g., Markov chains, Weibull distribution) for failure prediction over time.

4. Fuzzy Logic: Rule-based approach allowing reasoning under uncertainty, ideal for embedded systems.

5. Comparative Analysis

• ML: High accuracy; needs quality labeled data and retraining.

• Digital Twins: Comprehensive but resource-intensive.

• Stochastic Models: Statistically solid; less adaptive.

• Fuzzy Logic: Lightweight and real-time; limited accuracy without expert tuning.

6. Challenges and Limitations

Key barriers to adoption include:

• Data quality: Incomplete or noisy sensor data reduces model accuracy.

• Interoperability: Diverse automotive systems hinder seamless integration.

• Real-time constraints: Edge devices struggle with resource-heavy ML models.

• Cybersecurity: Increased connectivity heightens risk of data breaches.

• Economic constraints: High initial costs and unclear short-term ROI deter small manufacturers.

7. Future Trends

1. Edge Intelligence & Decentralized Learning: Faster local processing and privacy-preserving model training (e.g., federated learning).

2. Model Explainability: Need for interpretable AI to build trust and meet regulations.

3. EV-Specific Models: Tailored predictive tools for electric vehicles (e.g., battery health, thermal behavior).

4. Integration with Smart Factories: Seamless maintenance from production to in-use lifecycle.

5. Standardization: Development of open, interoperable IIoT platforms for broader adoption.

This summary encapsulates the transition from conventional to intelligent maintenance strategies in automotive systems, highlighting how IIoT, AI/ML, and new computing paradigms are revolutionizing vehicle upkeep and reliability.

Conclusion

Predictive maintenance, driven by the Industrial Internet of Things, represents a significant leap in automotive maintenance practices. It transforms how ve- hicles are monitored, diagnosed, and serviced—moving from reactive responses to proactive, data-driven decisions. This paper has presented a comparative survey of the primary methodolo- gies used in IIoT-based automotive predictive maintenance, including machine learning, digital twins, stochastic modeling, and fuzzy logic systems. While each technique has its unique advantages and trade-offs, their combined potential of- fers a robust framework for next-generation vehicle diagnostics. By highlighting architectural foundations, practical challenges, and future trends, this study aims to inform researchers, OEMs, and fleet operators about the strategic deployment of IIoT for smarter and more reliable vehicle mainte- nance.

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Copyright

Copyright © 2025 Akshay Hiremath, Amit Hatekar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.