Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0
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The document discusses the importance of real-time data processing for Industrial IoT and Manufacturing 4.0, emphasizing the role of Apache Kafka in stream processing for condition monitoring and predictive maintenance. It highlights various applications across industries such as shipping and oil and gas, showcasing the benefits of improved logistics, security, and innovation through data integration. Key features include real-time alerting and the use of AI and machine learning techniques to enhance operational efficiency.
Apache Kafka for Predictive Maintenance in Industrial IoT / Industry 4.0
- 1. The Rise of Data in Motion for Industrial IoT and Manufacturing 4.0 Condition Monitoring and Predictive Maintenance in Real-time with Stream Processing Kai Waehner Field CTO kai.waehner@confluent.io linkedin.com/in/kaiwaehner confluent.io kai-waehner.de @KaiWaehner
- 2. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Real-time Data beats Slow Data. Manufacturing Sensor diagnostics MES/ERP Integration Reporting Edge Computing Condition Monitoring Predictive Maintenance Quality Assurance Logistics Supply Chain Inventory management Track & Trace Context-specific routing Cybersecurity Threat detection Intrusion detection Incident response Fraud detection
- 3. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Industry Trends - Software and Digital Services become the Key Differentiator 3 https://www.mckinsey.com/industries/advanced-electronics/our-insights/iiot-platforms-the-technology-stack-as-value-driver-in-industrial-equipment-and-machinery
- 4. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Apache Kafka is the Platform for Data in Motion MES ERP Sensors Mobile Customer 360 Real-time Alerting System Data warehouse Producers Consumers Streams and storage of real time events Stream processing apps Connectors Connectors Stream processing apps Supplier Alert Forecast Inventory Customer Order 4
- 5. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Shipping Industry Marine, Oil Transport, Vessel Fleet, Shipping Line, Drones Real-time Operations, Logistics, Predictive Maintenance, Security Customer Data Crew, Cargo Vessel Data Fuel Consumption, Speed, Planned Maintenance Automatic Identification System (AIS) Unique Identification, Position, Course, Weather, Draft Drone Data Deliveries, Survey/Inspection of Assets such as Oil Rigs, Pipelines, Offshore Turbines Edge Analytics Bidirectional Edge to Cloud Integration Data Ingestion Stream Processing Data Integration Logistics Track&Trace Routing Monitoring Alerting Command&Control Batch Analytics Reporting Machine Learning Backend Systems Oracle, SAP, OSIsoft PI, etc. X = Event Streaming X = Other Technologies Bi-Directional Hybrid Cloud Replication
- 6. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Mercury Systems – Digital Thread Global technology company serving the aerospace and defense industry Digital Thread • Bring together design and product information across the product life cycle • Mendix based portal with combined data from PLM/ERP/MES • Confluent for cloud-native and reliable real-time event streaming across applications • Apply AI and ML techniques • All data in one location viewed with common tools Benefits • Faster time to market • Ability to scale • Improved innovation pipeline and reduced cost of failure https://www.confluent.io/events/kafka-summit-americas-2021/driving-a- digital-thread-program-in-manufacturing-with-apache-kafka/
- 7. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Devon Energy Oil & Gas Industry Improve drilling and well completion operations Edge stream processing/analytics + closed-loop control ready Vendor agnostic (pumping, wireline, coil, offset wells, drilling operations, producing wells) Replication to the cloud in real-time at scale Cloud agnostic (AWS, GCP, Azure) Source: Energy in Data - Powered by AAPG, SEG & SPE: energyindata.org
- 8. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Severstal – Real-time Shop Floor Predictive Maintenance and Quality Assurance at the Shop Floor Real Time Streaming Machine Learning with Kafka
- 9. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Condition Monitoring and Predictive Maintenance Stateless and stateful stream processing for real-time data correlation with Kafka-native tools (Kafka Streams / ksqlDB) 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 3 4 5 6 7 2 1 Time Sensor Events
- 10. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Condition Monitoring and Predictive Maintenance Stateless and stateful stream processing for real-time data correlation with Kafka-native tools (Kafka Streams / ksqlDB) 8 9 1 0 1 1 1 2 1 3 1 4 1 5 3 4 5 6 7 2 1 Time Sensor Events 1 6 Condition Monitoring (Temperature Spikes) Stateless Filter Above-Threshold Events Streams builder .stream(”temperature-sensor") .filter((key, sensor-data) -> sensor-data.temperature > 100) .to(”temperature-spikes");
- 11. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Condition Monitoring and Predictive Maintenance Stateless and stateful stream processing for real-time data correlation with Kafka-native tools (Kafka Streams / ksqlDB) 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 3 4 5 6 7 2 1 Time Sensor Events Predictive Maintenance (Continuous Anomaly Detection) Stateful Correlation of Events CREATE TABLE anomaly_detection AS SELECT temperature_spike_id, COUNT(*) AS total_spikes, AVG(temperature) AS avg_temperature FROM sensor-data WINDOW TUMBLING (SIZE 1 HOUR) GROUP BY temperature_spike_id EMIT CHANGES;
- 12. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Condition Monitoring and Predictive Maintenance Stateless and stateful stream processing for real-time data correlation with Kafka-native tools (Kafka Streams / ksqlDB) 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 3 4 5 6 7 2 1 Time Sensor Events Predictive Maintenance (Continuous Anomaly Detection) Real-time Machine Learning CREATE STREAM anomaly_detection AS SELECT sensor_id, detect_anomaly(sensor_values) FROM machine; TensorFlow model embedded in User Defined Function (UDF)
- 13. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Condition Monitoring and Predictive Maintenance Stateless and stateful stream processing for real-time data correlation with Kafka-native tools (Kafka Streams / ksqlDB) 8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 3 4 5 6 7 2 1 Condition Monitoring (Temperature Spikes) Time Predictive Maintenance (Continuous Anomaly Detection) Stateful Correlation of Events Stateless Filter Above-Threshold Events Streams Sensor Events
- 14. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT CRM Real-Time Location System (RTLS) for Asset Tracking Customer data Advanced Planning and Scheduling (APS) Manager Get report API Customer Customer Customer data Truck schedule Payment data Route details Streams of real time events Customer data Train schedule Payment data Loyalty information Streams of real time events Customer data Train schedule Payment data Loyalty information Streams of real time events Event Streaming in Hybrid Industrial IoT Wavelength Public Cloud VPC Campus 5G Telco Carrier 5G Smart Factory Edge Computing
- 15. @KaiWaehner - www.kai-waehner.de - Data in Motion for the Industrial IoT Car Engine Car Self-driving Car Confluent completes Apache Kafka. Cloud-native. Everywhere.