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Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

Author

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  • Ali S. Allahloh

    (Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India)

  • Mohammad Sarfraz

    (Department of Electrical Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh 202002, India)

  • Atef M. Ghaleb

    (Department of Industrial Engineering, College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia)

  • Abdullrahman A. Al-Shamma’a

    (Electrical Engineering Department, College of Engineering, Imam Mohammed Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia)

  • Hassan M. Hussein Farh

    (Electrical Engineering Department, College of Engineering, Imam Mohammed Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia)

  • Abdullah M. Al-Shaalan

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite resources. In the realm of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies, Caterpillar (CAT) generator set (genset) operations have been revolutionized, unlocking unprecedented fuel savings and reducing environmental harm. Envision a system that not only enhances fuel efficiency but also anticipates maintenance needs with state-of-the-art technology. This standalone IIoT platform crafted with Visual Basic.Net (VB.Net) and the KEPware Object linking and embedding for Process Control (OPC) server gathers, stores, and analyzes data from CAT gensets, painting a comprehensive picture of their inner workings. By leveraging the Modbus Remote Terminal Unit (RTU) protocol, the platform acquires vital parameters such as engine load, temperature, pressure, revolutions per minute (RPM), and fuel consumption measurements, from a radar transmitter. However, the magic does not stop there. Machine Learning.Net (ML.Net) empowers the platform with machine learning capabilities, scrutinizing the generator’s performance over time, identifying patterns and forecasting future behavior. Equipped with these insights, the platform fine tunes its operations, elevates fuel efficiency, and conducts predictive maintenance, minimizing downtime and amplifying overall efficiency. The evidence is compelling: IIoT and AI technologies have the power to yield substantial fuel savings and enhance performance through predictive maintenance. This research offers a tangible solution for industries eager to optimize operations and elevate efficiency by embracing IIoT and AI technologies in CAT genset operations. The future is greener and smarter, and it starts now.

Suggested Citation

  • Ali S. Allahloh & Mohammad Sarfraz & Atef M. Ghaleb & Abdullrahman A. Al-Shamma’a & Hassan M. Hussein Farh & Abdullah M. Al-Shaalan, 2023. "Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance," Sustainability, MDPI, vol. 15(11), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8808-:d:1159387
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    References listed on IDEAS

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    1. Sasanka Katreddi & Sujan Kasani & Arvind Thiruvengadam, 2022. "A Review of Applications of Artificial Intelligence in Heavy Duty Trucks," Energies, MDPI, vol. 15(20), pages 1-20, October.
    2. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
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