Artificial Intelligence Research Reveals Transformative Role of Predictive Maintenance in Industrial Processes

Strategic Insights -

Researchers at the Chitkara University Institute of Engineering and Technology have published a new report highlighting the impact of artificial intelligence on industrial processes. According to the study, the Industrial Internet of Things is transforming industrial processes by integrating edge, fog, and cloud computing to offer predictive maintenance and reduce unexpected downtime. The research proposes a novel multilayer framework that uses MQTT protocol for transferring data across layers and integrating pre-processing and machine learning algorithms to ensure robust and accurate predictions. The study also discusses the key enabling technologies, including IoT sensors, edge computing, fog computing, cloud computing, machine learning, and blockchain, and highlights the challenges such as energy efficiency and system scalability.

Key Takeaways:

  • The Industrial Internet of Things is transforming industrial processes through the integration of edge, fog, and cloud computing to offer predictive maintenance.
  • The proposed multilayer framework uses MQTT protocol for transferring data across layers and integrates pre-processing and machine learning algorithms to ensure robust and accurate predictions.
  • The study highlights the rapid growth in IIoT-based predictive maintenance research, with India and China emerging as leaders in contributions and citations.
  • The applications of predictive maintenance in manufacturing, energy, and automotive industries point to extensive opportunities for reducing downtime, enhancing equipment life, and reducing operational costs.
  • The research paves the way for scalable, energy-efficient, and sustainable predictive maintenance systems to advance the next generation of industrial processes.
  • The proposed framework is designed to overcome challenges such as energy efficiency and system scalability.
  • The study discusses the enabling technologies, including IoT sensors, edge computing, fog computing, cloud computing, machine learning, and blockchain.

Statistics:

  • 1,281 publications were analyzed between 2015 and 2024 to show the rapid growth in IIoT-based predictive maintenance research.
  • India and China are emerging leaders in contributions and citations in IIoT-based predictive maintenance research.
  • The proposed framework uses machine learning algorithms to ensure robust and accurate predictions.
  • The study highlights the challenges of energy efficiency and system scalability in industrial processes.

Sources:

  • Analyzing the impact of edge, fog and cloud computing on predictive maintenance in the Industrial Internet of Things. Discover Computing, 2025,28(1):1-42.
  • Chitkara University Institute of Engineering and Technology.
  • Dimple Kapoor, Corresponding Author. Chitkara University Institute of Engineering and Technology.
  • Deepali Gupta, Author. Chitkara University Institute of Engineering and Technology.
  • Mudita Uppal, Author. Chitkara University Institute of Engineering and Technology.
  • Springer (Publisher).