Industrial IoT & Industry 4.0 Use Cases

Integrate machine data, historians, SQL servers, MES, ERP systems.

Length: 1:50

Distribution platform for your custom algorithms and code. Test, deploy and maintain. At scale.

Length: 2:13

Collect raw machine data and send to any Cloud service or storage.

Length: 1:25

Filter, normalize and harmonize machine data close to the machines. Prepare for a common data model.

Length: 2:25

Remote assets. Look deeper. Save relevant data. Act on anomalies

Length: 2:05

Reduce storage and processing costs up to 99% with smart filtering and aggregation. Bypass expensive cloud entry services.

Length: 2:06

Collect data from one or more machines, apply some logic and send the result to the same or different machines. Optimize performance or act on anomalies.

Length: 1:47

Separation of machine data for delivery of data to machine builders/OEMs and other external users.

Length: 1:30

Message broker built for the shop floor to simplify machine communication and system integration.

Length: 1:34

Leverage off-the-shelf cameras, find anomalies and integrate triggers.

Length: 1:32

Use cameras to detect when people or object move in or out of areas. Warn intruders or stop machines to avoid incidents.

Length: 1:32

• The Power of Machine Learning and How to create value
• Main Steps to Get Started with Machine Learning
• 2 real life use case examples from Crosser Customers
• Overview of the Crosser low-code Platform

The purpose built platform for simplifying advanced analytics, machine learning, integration and automation of industrial data.

• Why Transforming, Cleaning & Normalization of IOT Data?
• Benefits of Flow Based Programming for Industry 4.0
• Typical Transforming, Cleaning & Normalization operations for Industrial IoT

• Project Background: Strategic need for simplicity, flexibility, and reliability in digital transformation.
• The Node-RED transition: Blechwarenfabrik Limburg seamlessly transitioned from Node-RED to Crosser in under 5 weeks for 14 production lines.
• Value and Outcomes: Reduced downtimes, cut costs, and improved overall efficiency.
• Future Innovations: Power monitoring, predictive maintenance, and anomaly analysis.

• Why Operational KPI’s and OEE?
• Breakdown of OEE - three KPI’s in one
• OEE on streaming data - utilizing the edge for real-time insights

• Collect and prepare data to deliver results with your ML model
• Test and train ML models for edge deployments
• Deploy and maintain your ML model on one or many devices

• Introduction to the OPC reader and OPC writer module in Crosser Flow Studio
• Set up a working machine to machine data flow
• Remotely test and debug the M2M data flow

• Examples of anomalies and how these can be detected using streaming analytics
• How the Crosser Edge Analytics solution simplifies the implementation and roll-out of anomaly detection close to your machines

• How to design good & robust ML models
• How to utilize ML in cameras and vision sensors
• MLOps, orchestration and maintenance of Vision ML at scale
• 3 Example use cases

• Setting up the right infrastructure - data pipeline - for your AI project
• How to choose a development Strategy for different AI applications
• How to scale your AI project into production

• How edge analytics can enhance and simplify factory floor integrations
• How the Crosser Edge Analytics solution empowers non-developers to innovate faster than ever

• Why Transforming, Cleaning & Normalization of IoT Data
• How to build a flexible digital infrastructure
• Common use cases for data transformation, cleaning and normalization
• 3 real life use case examples from Crosser Customers

• Why is OPC having a central role in IIoT?
• How to overcome to most common challenges with OPC integrations
• Example 1 - OPC Use case: Stream OPC data to cloud
• Example 2 - OPC Use case: Batch upload OPC data to centralized storage
• Example 3 - OPC Use case: Closed-loop process optimization using ML
• Example 4 - OPC Use case: Monitoring your machines using OPC alarms

• Introduction to Crosser’s pre-built connectors for various SQL databases
• Understand Edge to SQL integrations
• Learn how to stream any data in and out of your SQL environment
• Understand how to trigger stored procedures and use SQL statements in the Crosser Low Code environment

• The concept of Data Driven Stream Processing
• How to enrich your data tags with metadata using Crosser
• How to use metadata for tag specific processing
• Example 1 - Energy Meter integration
• Example 2- Data routing and processing for streaming data
• Q&A