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

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.
• A novel multilayer framework proposed in the study uses MQTT protocol for transferring data across layers and integrating pre-processing and machine learning algorithms to ensure robust and accurate predictions.
• The study discusses the key enabling technologies, including IoT sensors, edge computing, fog computing, cloud computing, machine learning, and blockchain.
• India and China are emerging leaders in contributions and citations in IIoT-based predictive maintenance research.
• The applications of predictive maintenance in the manufacturing, energy, and automotive industries point to extensive opportunities for reducing downtime, enhancing the life of equipment, and reducing operational costs.
• The proposed framework highlights the transformative role of predictive maintenance in modernizing industrial ecosystems.
• The study paves the way for scalable, energy-efficient, and sustainable predictive maintenance systems to advance the next generation of industrial processes.

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 study proposes a framework that uses real-time data collection, low-latency processing, and advanced analytics to predict and prevent equipment failures.
• The framework is designed to be scalable, energy-efficient, and sustainable to advance the next generation of industrial processes.

Sources:

• Discover Computing, "Analyzing the impact of edge, fog and cloud computing on predictive maintenance in the Industrial Internet of Things," 2025, 28(1): 1-42.
• Springer, "Analyzing the impact of edge, fog and cloud computing on predictive maintenance in the Industrial Internet of Things," 2025, 28(1): 1-42.