Predictive Maintenance and Real Time Monitoring using IoT and Cloud Computing

Industrial Revolution 4.0 describes the rapid transformation that industry is going through due to Artificial Intelligence (AI), Machine Learning (ML) and IoT (Internet of Things). Industrial IoT (IIOT) combines traditional industrial processes with modern technology such as sensors, data analytics,...

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Vydáno v:2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN) s. 814 - 820
Hlavní autoři: Suthar, Aaryan, Kolhe, Kishor, Gutte, Vitthal, Patil, Dhanashri
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 03.07.2024
Témata:
Artificial Intelligence
Cloud computing
Conditional Monitoring
Data analysis
Data visualization
Industrial IoT
IoT
Machine learning
OEE
predictive maintenance
Real-time systems
Sensor systems
Sensors
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Shrnutí:Industrial Revolution 4.0 describes the rapid transformation that industry is going through due to Artificial Intelligence (AI), Machine Learning (ML) and IoT (Internet of Things). Industrial IoT (IIOT) combines traditional industrial processes with modern technology such as sensors, data analytics, and machine learning to optimize and automate processes. Like the Industrial Revolution, IIoT is transforming the way that goods are produced, but with some significant differences. IoT enables machines and devices to communicate with one another in real-time, providing valuable insights into the performance of industrial processes. This allows for predictive maintenance, where machines can be repaired or replaced before they fail, reducing downtime and maintenance costs. OEE (Overall Equipment Efficiency), an industry standard for gauging production efficiency, is raised with the use of predictive maintenance. By employing IoT, cloud computing, machine learning and real-time monitoring to identify abnormalities, malfunctions, and errors in manufacturing equipment, this paper seeks to boost OEEas a result of predictive maintenance. Sensors are employed in equipment to identify motion and other crucial data, which is subsequently transmitted to the cloud for analysis. As a result of this work, equipment dependability and efficiency are increased while downtime and maintenance costs are decreased.
DOI:10.1109/ICIPCN63822.2024.00141