Design and Implementation of an Intelligent Data Pipeline for Predictive Maintenance in a Smart Product Integrated by IoT
Keywords:
Predictive Maintenance, IoT, Smart Products, Big DataAbstract
Particularly with the growing integration of the Internet of Things (IoT), predictive maintenance is essential for ensuring the optimal performance and longevity of new products in the present industrial environment. The design and implementation of a data pipeline enabling predictive maintenance in an IoT-enabled luminous product ecosystem is described in this paper. To foretell failures ahead and thus reduce downtime and maintenance costs, the proposed data pipeline effectively combines real-time sensor data, cloud storage, and machine learning algorithms. Our approach begins with data collecting from IoT sensors included into sophisticated products, including pressure, vibration, and temperature readings. We review the architecture for this pipeline including MQTT, database management, and cloud services and communication protocols. Furthermore addressed are data latency, scalability, and the seamless interaction of edge devices with the cloud. Our technology offers expected insights based on historical data and real-time inputs, therefore helping maintenance staff to carry out preventive actions.
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