A Study on Industrial Process Optimisation and Predictive Maintenance using AI-mRL

Abstract

Industrial 4.0 automation environment integrates a combination of mechanical devices, process machinery, Inductive and resistive loads that are monitored and operated through sensors, PLCs, PAC and Industrial IOT (IIOT) devices for better process and efficient operations. Creating a seamless operating environment requires a robust and most advanced technology integration and data mining environment. Our Research uses multiple fusion sensing hardware and embedded programming with Deep learning and AI algorithms to predict industrial process/machine failures using, ? An Embedded AI edge device (hardware). ? Long distance Sub-GHz communication protocol. ? AI agent to collect, analyse and tigger faults on local and cloud infrastructure. The hardware uses a micro controller with Wi-Fi communication to collect and transmit data. The system captures data on multiple sensing elements including Power, Vibrational and temperature for smart analytics to sense and report failure mode trigger to avoid process failures and machine breakdown. The research will understand the industrial process efficiency and performance by collecting data from multiple sensors for a data driven approach using Machine learning and deep learning algorithm. “A fault trigger before the machine breakdown”, that minimise the cost and improve the process efficiency using Machine Reinforcement Learning (mRL).

Introduction

1. What is Edge AI?

• Edge AI refers to embedding artificial intelligence (AI) directly into edge devices (like microcontrollers), enabling them to process data locally without relying on cloud computing.

• Traditional AI models depend on cloud infrastructure, which introduces latency and connectivity dependency.

• Edge AI allows devices to collect, analyze, and act in real-time, with data optionally sent to the cloud for further analysis or long-term storage.

2. Edge AI in Industrial Automation

• In Industry 4.0, Edge AI is transforming industrial automation by adding intelligence at the device level, improving responsiveness, efficiency, and adaptability.

• Replaces static logic in PLC/PAC systems with dynamic, AI-driven decision-making.

• Benefits include:

  • Improved process efficiency via smart sensors and AI models

  • Enhanced predictive maintenance using historical machine data and machine reinforcement learning (mRL)

  • Energy savings by reducing machine idle time and optimizing operations

3. Literature Review Highlights

• Predictive maintenance is a major benefit of AI in industry, helping reduce equipment downtime and optimize maintenance.

• AI and ML models (including anomaly detection and multi-agent reinforcement learning) improve forecasting and maintenance scheduling.

• Key challenges:

  • Need for large, high-quality datasets

  • Technical complexity in deploying ML systems

  • Shortage of skilled professionals in AI integration

4. Key Problems in Manufacturing

A. Data Collection Challenges

• Manufacturing collects vast data from IoT sensors, meters, and machines, but this data is often siloed and not leveraged effectively.

• Electrical and environmental data is underutilized for optimization.

• Extracting and aligning relevant data for ML is difficult due to fragmented sources.

B. Legacy Systems Integration

• Older machines and protocols are incompatible with modern data platforms.

• Lack of standardization and integration expertise creates barriers to digital efficiency.

• Requires knowledge of legacy hardware behavior and middleware communication for integration.

5. Research and Technological Approach

• AI models are trained using high-quality, cleaned datasets and deployed at the edge level for fast, local decision-making.

• Modern edge devices use GPUs and parallel computing to run advanced neural networks.

• Technologies like robotic process automation (RPA), smart cameras, and wireless sensors enhance data collection and processing.

6. Business Case and Adoption

A. AI-Controlled Smart Processes

• AI improves decision-making through real-time data analysis and digitalization of factory processes.

• Running AI at the edge:

  • Reduces cloud storage and latency

  • Enhances cybersecurity through decentralized processing

  • Enables local mesh networks for synchronized, parallel data processing

B. Predictive Maintenance with mRL

• Uses machine reinforcement learning to predict and prevent equipment failures based on sensor data (e.g., vibration, temperature, load).

• Outcomes:

  • Less downtime

  • Increased equipment lifespan

  • Lower spare inventory and repair costs

  • Improved energy efficiency and reliability

7. Key Deliverables

• Fault detection before machine failure, reducing operational disruption.

• Process and energy optimization using methods like Particle Swarm Optimization (PSO).

8. Digital Twin in Industrial Operations

A. What is a Digital Twin (DT)?

• A DT replicates a physical asset or process in a virtual environment using real-time data from IoT sensors.

• Offers remote, 24/7 monitoring, simulation, and control of operations.

• Helps test and visualize process improvements before physical implementation.

B. Integration with Edge AI

• Edge devices form sub-digital twins, which are combined into a full DT for advanced simulation.

• Graphical rendering allows real-time data visualization and rollbacks.

• Enables a scalable, interactive, and fully automated industrial environment.

Conclusion

In conclusion, the integration of advanced technology such as AI, deep learning algorithms, and edge devices into the industrial environment has enabled the prediction of process failures and the optimization of machine efficiency through predictive maintenance. By leveraging quality data sets and machine reinforcement learning, this approach not only minimizes costs and prevents machine breakdowns but also improves process efficiency and energy savings. The utilization of AI-controlled smart processes and digitalization, along with the development of digital twin technology, allows for real-time data analysis, better decision-making, and enhanced operational efficiency in industrial automation. Moreover, the implementation of predictive maintenance using AI-MRL enhances production hours, reduces downtime, increases system reliability, and saves on maintenance costs. Overall, the adoption of edge AI and advanced technologies offers a transformative solution for achieving seamless and efficient industrial processes while paving the way for smart factories and automated decision-making capabilities.

References

[1] Zhang, W., Yang, D., & Wang, H. (2020). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 9(1), 112-123. [2] Smith, A., Jones, B., & Lee, C. (2018). Recurrent neural networks for predictive maintenance in manufacturing. Journal of Manufacturing Processes, 35, 102-115. [DOI: 10.1016/j.jmapro.2017.12.008] [3] Zhan, J.; Ge, X.J.; Huang, S.; Zhao, L.; Wong, J.K.W.; He, S.X. Improvement of the inspection-repair process with building information modelling and image classification. Facilties 2019, 37, 395–414. [4] Zhang, W., Yang, D., & Wang, H. (2020). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Systems Journal, 9(1), 112-123. [5] Gunay, B.; Shen, W.; Yang, C. Text-mining building maintenance work orders for component fault frequency. Build. Res. Inf. 2018, 47, 518–533 [6] Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510. [7] Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. Vol. 1 No. 1 (2024): International Journal of Advanced Engineering Technologies and Innovations [8] Ravi, V., & Sinha, R. (2021). Predictive maintenance in manufacturing: A machine learning approach. Indian Journal of Industrial Relations, 56(3), 123-134. [9] Sharma, P., & Kumar, A. (2020). Optimization of production processes using AI. Journal of Manufacturing Technology Management, 31(5), 897-910. [10] Nair, S., & Rao, T. (2019). AI in predictive maintenance: Case studies from the Indian automotive industry. Automotive Manufacturing, 18(2), 45-60. [11] Mehta, A., & Joshi, M. (2021). Role of data analytics in predictive maintenance. International Journal of Services and Operations Management, 37(1), 84-97. [12] Verma, R., & Gupta, N. (2022). Implementing mRL for process optimization in Indian textile industry. Textile Progress, 54(1), 1-15. [13] Zhang, X., & Wang, Y. (2021). AI-driven predictive maintenance: A systematic review. Journal of Manufacturing Systems, 58, 25-34. [14] Lee, J., & Kim, H. (2020). Reinforcement learning for optimizing industrial processes. IEEE Transactions on Automation Science and Engineering, 17(2), 982-993. [15] Patel, R., & Chen, J. (2019). Intelligent predictive maintenance: An AI perspective. International Journal of Production Research, 57(11), 3371-3388. [16] Garcia, R., & Martinez, J. (2022). Enhancing predictive maintenance with machine learning: A case study. Journal of Quality in Maintenance Engineering, 28(1), 12-22. [17] Nguyen, H., & Tran, L. (2021). AI for predictive maintenance in smart factories. Journal of Industrial Information Integration, 22, 100-115. [18] Khan, M., & Malik, S. (2020). Optimizing manufacturing processes using AI techniques. Expert Systems with Applications, 135, 120-130. [19] Huang, J., & Zhou, Y. (2019). Machine learning applications in predictive maintenance: A comprehensive review. Journal of Manufacturing Science and Engineering, 141(7), 1-15.

Copyright

Copyright © 2025 Sridharan Ganesan. 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.