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Using Confluent Cloud's managed Kafka clusters and Flink service, we can analyze factory floor robot telemetry data to identify performance bottlenecks or potential equipment failures, enabling proactive maintenance and optimized robot performance.
Modern automated manufacturing factory floors can have hundreds of robots performing various tasks, including picking and placing objects, assembling parts, packaging, welding, painting, and cleaning. By effectively utilizing telemetry data and leveraging the power of real-time streaming analytics platforms like Confluent Kafka clusters and Flink SQL, valuable insights into robot health and performance can be gained.
This use case leverages the following building blocks in Confluent Cloud:
Here's a look at the reference architecture for optimizing robot performance on the factory floor for streaming analytics in real time with Confluent Cloud and Flink:
1. HiveMQ, MQTT Broker: A central hub for robot communication. Resource-constrained robots can efficiently publish telemetry data (kinematics, motor and performance reading) and receive commands (e.g., anomaly or maintenance alerts) using the lightweight MQTT protocol. HiveMQ's scalability and security features ensure reliable data exchange for a large fleet of robots, keeping manufacturing operations running smoothly.
2. HiveMQ Clients: HiveMQ client libraries provide functionality to publish messages (simulating robot telemetry data) and subscribe to messages on a topic.
3. Enterprise Extension for Kafka: An extension for the HiveMQ MQTT broker bridges the gap between MQTT and Apache Kafka. This Enterprise Extension for Kafka enables bidirectional communication between devices and applications using different messaging protocols:
— MQTT to Kafka: Ingests robot telemetry data streams into Kafka.
— Kafka to MQTT: Subscribes to relevant Kafka topics to receive alerts.
4. Confluent Cloud: Confluent Cloud provides a hosted platform for the Kafka cluster and Flink compute pools that will be used to analyze and aggregate the data.
For stream processing various functions of Flink SQL used as avg, windowing, group by, filtering, case-when, etc.
Part 1 - Setting Up Data Flow
Part 2 - Real-Time Anomaly Detection
Part 3 - Alerting via MQTT
Contact Ness to learn more about this use case and get started.