Introduction
Programmable Logic Controllers (PLCs) are critical components in many industrial automation and control systems. Rockwell Automation (RA) is a leading global provider of industrial automation solutions, including PLCs. These PLCs generate vast amounts of valuable data that can provide major insights into operations, productivity, efficiency, and more.
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Integrating RA PLC data with AWS cloud services unlocks the potential to apply advanced analytics, ensure data security, scale easily, and reduce costs.
EtherNet/IP is an industrial Ethernet network protocol that enables real-time, high-speed communication between RA PLCs and other devices. It provides an open standard method for transferring data from PLCs to IT systems like the AWS cloud.
This guide will cover the fundamentals of collecting and uploading RA PLC data to AWS using EtherNet/IP. It will explain the benefits, outline the prerequisites, and provide step-by-step instructions for setting up the environment, collecting and uploading the data, storage and analysis options, real-world examples, and troubleshooting advice.
What is RA PLC Data?
RA PLCs control industrial processes by executing ladder logic programs. As PLCs monitor and control equipment, they gather valuable data on process parameters like temperature, pressure, flow rate, cycle times, machine states, faults, alarms, and more.
This RA PLC data reflects the real-time status and performance of machines and processes. When aggregated over time, the data forms a comprehensive record of overall equipment effectiveness (OEE), production counts, uptimes/downtimes, and other key metrics.
Some examples of RA PLC data include:
- Sensor measurements like temperature, speed, or pressure
- Current operating status and modes
- Faults, errors, and alarm codes
- Cycle times and equipment usage
- Input/output values
- Histories and trend data
This data is crucial for monitoring, controlling, optimizing, and improving industrial automation systems. But RA PLCs have limited data storage and analysis capabilities natively. Uploading RA PLC data to AWS unlocks more potential value from the data.
What is AWS?
Amazon Web Services (AWS) is a cloud computing platform that provides on-demand delivery of IT resources and applications via the internet. This includes databases, storage, analytics, machine learning, and more—all managed as services in the cloud.
Some key benefits of using AWS include:
- Global infrastructure – AWS data centers are located worldwide so you can deploy applications to your users quickly.
- Flexible scalability – Services can scale on-demand to meet spikes and fluctuations in traffic.
- Security – AWS provides a wide array of security services to protect sensitive data.
- Cost savings – Pay only for the resources used rather than investing in local servers.
- Frequent updates – AWS is continuously enhanced with new capabilities.
By uploading RA PLC data into AWS services like S3, RDS, DynamoDB, EMR, QuickSight and more, users can unlock deeper insights and scale as needed. AWS also offers tight integration with industrial data platforms like GE Predix, PTC ThingWorx, and Siemens Mindsphere.
What is EtherNet/IP?
EtherNet/IP is an industrial network protocol that enables real-time communication between industrial devices like PLCs, drives, sensors, and HMIs. It builds on standard Ethernet hardware and TCP/IP to provide a flexible method of transferring control and information data.
Key capabilities of EtherNet/IP include:
- Use of standard Ethernet technologies for interoperability.
- Seamless integration between factory floor devices and higher-level networks.
- Messaging services for I/O, configuration, alarms, and more.
- Options for both real-time control and information messaging.
- Common industrial protocol code and functions.
- Suitability for discrete, process, batch, motion, safety, and drive-based systems.
EtherNet/IP acts as the enabling technology for collecting data directly from RA PLCs and sending it to AWS. Within the AWS environment, the EtherNet/IP protocol can be implemented in a variety of ways to transfer RA PLC data over the network.
Why Collect and Upload RA PLC Data to AWS?
Migrating RA PLC data to AWS cloud-based services delivers a multitude of benefits:
- Data consolidation – Combine data from multiple sites and systems into one location. Break down data siloes.
- Improved data security – Protect critical production data more reliably. AWS provides sophisticated user access controls, encryption, backup/recovery, and cybersecurity tools.
- Enhanced scalability – AWS handles fluctuating data volumes, bandwidth needs, and users easily. Add storage and analysis capacity on-demand.
- Advanced analytics – Uncover insights with AWS analytics, business intelligence, and machine learning services using historical RA PLC data.
- Reduced costs – Avoid large capital investments in on-premise IT infrastructure. Pay for only what you use in the cloud.
- New IoT capabilities – Unlock edge computing, remote monitoring, predictive maintenance, and other IoT use cases by connecting RA PLCs to AWS IoT services.
- Improved efficiency – Optimize production processes and asset utilization based on RA PLC data analysis in the cloud.
- Enhanced integration – Leverage many third-party apps and services via the AWS cloud ecosystem.
By migrating RA PLC data storage, processing, visualization, and analysis to the cloud, manufacturers can gain deeper operational insights while reducing IT costs.
Benefits of Collecting and Uploading RA PLC Data to AWS
Collecting and uploading RA PLC data into AWS delivers several advantages:
- Increased data visibility – Cloud-based data aggregation provides enterprise-wide visibility into all systems and locations. Identify data correlations across siloed sources.
- Flexibility – Users can access data anytime, anywhere via the cloud. APIs enable easy integration with other apps and services.
- Easy scalability – Start small and scale up storage, bandwidth, analytics capability, and users as needed, avoiding upfront investments. Pay only for what is used.
- Disaster recovery – Data uploaded to AWS is backed up securely in multiple data centers, enabling quick recovery from outages.
- Enhanced security – AWS offers robust, enterprise-grade security tools and practices to protect production data in the cloud.
- Cost savings – Avoid purchasing expensive local servers and data center infrastructure by leveraging managed cloud services instead.
- Operational insights – Cloud data analytics tools uncover hidden factors impacting OEE, yield, quality, safety, and profitability.
- New revenue streams – Monetize RA PLC data by using AWS analytics and machine learning to develop value-added services.
- Legacy modernization – Extend the capabilities and value derived from legacy RA PLCs rather than replacing them.
Migrating to AWS services creates opportunities to optimize operations, develop new data-driven services, and save on IT costs.
Prerequisites for Collecting and Uploading RA PLC Data to AWS
Several key elements need to be in place before implementing an AWS cloud data architecture with RA PLCs:
- RA PLCs – Existing Allen-Bradley or ControlLogix PLCs on a ControlNet or Ethernet/IP network.
- AWS account – Sign up for an AWS account to access services like S3, RDS, DynamoDB, EMR, QuickSight, and more.
- AWS connectivity – Ensure RA PLC network access to AWS over the internet, via SD-WAN or AWS Direct Connect.
- EtherNet/IP capability – Add EtherNet/IP communication capability to the RA PLCs if needed, using add-on modules or updating firmware.
- Bandwidth management – Bandwidth between sites and AWS needs to match data generation rates. Optimize connectivity as needed.
- Data security – Establish data security, users, roles, rights, and access policies for AWS. Enable encryption.
- Data storage – Select and configure the appropriate AWS data storage service based on data types, volumes, and retention needs.
- Data processing – Choose AWS data analytics, business intelligence (BI), and machine learning services to implement based on use cases.
With the proper environments, tools, and connectivity in place, RA PLC data can securely flow from industrial assets into AWS for storage, processing, and analysis in the cloud.
Setting Up the Environment
To enable RA PLC data uploads to AWS, the source PLC environment and target AWS cloud environment must be properly configured. This involves setting up the PLC network, establishing AWS connectivity, deploying hardware and software components, and more.
Configuring the RA PLC
On the PLC side, key steps include:
- Enable EtherNet/IP – For legacy PLCs, add EtherNet/IP communication modules, adapters, or update firmware. Choose hardware to match bandwidth needs.
- Select data sources – Determine which PLC tags, registers, files, etc. will supply data to AWS. This is driven by the use cases and analytics requirements.
- Configure tags – Define any new tags required in the PLC logic to represent data values that will be transmitted to the cloud.
- Set up messaging – Establish appropriate messaging mechanisms on the PLC to transfer data over EtherNet/IP to the gateway connecting to AWS.
- Enable security – Implement cybersecurity controls on the PLC to protect from vulnerabilities and malicious attacks. Use Defense-in-Depth practices.
Properly preparing RA PLCs ensures reliable generation of quality data for AWS upload.
Configuring the AWS Environment
On the AWS side, key steps include:
- Create an AWS account – Sign up for a user account with appropriate permissions and policies for the required services.
- Select data storage – Choose AWS data storage services like S3, RDS, DynamoDB based on data types, volumes, and query needs.
- Configure security – Establish fine-grained user access controls, roles, passwords, groups, VPNs, encryption, etc. to protect data.
- Provision services – Deploy required AWS services for the intended use cases such as analytics, machine learning, BI, etc.
- Set up networking – Ensure secure VPN connection between the RA PLC network and AWS account using AWS Direct Connect or similar service.
- Verify tools – Test capabilities of chosen AWS services using sample RA PLC data for storage, processing, visualization, and analysis.
This prepares a tailored, secure AWS environment to receive, process, and provide access to RA PLC data from the industrial facility.
Installing and Configuring the Necessary Software
To connect the RA PLC data sources with the AWS cloud destination, appropriate software needs to be implemented:
- EtherNet/IP SDK – Software Development Kit to enable sending EtherNet/IP packets from the gateway to AWS.
- AWS SDK – Software Development Kit providing APIs to interface with AWS services from the gateway.
- Gateway agent – Software on an edge gateway device to handle EtherNet/IP and AWS SDK integration tasks.
- Collection app – Application to pull desired data from RA PLC and assemble into AWS upload packets.
- Storage app – Software to receive and store data packets in the selected AWS services such as S3.
Proper configuration and testing ensures efficient transfer of RA PLC data into the cloud. Regular software maintenance and patching is recommended.
Collecting RA PLC Data
There are several methods to collect data from Allen-Bradley and ControlLogix PLCs over EtherNet/IP for uploading to AWS:
Different Methods for Collecting RA PLC Data
- PLC tags – Read/write tags mapped to memory locations containing desired data values.
- Message instructions – MSG instructions read status of data table elements.
- Embedded database – SQL calls retrieve data values from the PLC database.
- Log files – Read sequential files that store historical trend data.
- CIP messaging – Send explicit CIP messages to collect data from PLC objects.
- OPC UA – Use OPC Unified Architecture to gather data from PLC.
- PlantPAx – Leverage the PlantPAx system’s distributed control capabilities.
Each approach has advantages and disadvantages depending on bandwidth, data types, frequency, volume, and integration requirements.
Choosing the Right Method for Your Needs
Factors guiding data collection method selection include:
- Data types – Some methods work better for real-time vs history data.
- Bandwidth – Determine required frequency, packet size, and network capacity.
- IT integration – Choose method that easily interfaces with AWS and other systems.
- Scalability – Select a method that can handle increased future data loads.
- Security – Pick a method that aligns with cybersecurity practices and policies.
- Costs – Weigh hardware and development costs against capabilities.
Analyze the use cases, data profiles, volumes and network considerations to pick the optimal collection method.
Configuring the Data Collection Process
Once a collection method is chosen, the process needs to be configured through:
- Agent parameters – Set gateway agent to match PLC hardware, tags, limits, speeds, etc.
- AWS client setup – Configure AWS SDK with access keys, region, service endpoints, etc.
- Collection tuning – Tweak collection frequency, batch size, buffers, etc. for smooth data flow.
- Error handling – Incorporate appropriate error detection, correction, alerts, and logging.
- Security – Implement robust user access, data encryption, VPNs, backups and other security measures.
- Monitoring – Track metrics on data collection rates, lags, bottlenecks and other issues.
- Maintenance – Perform periodic software updates, hardware checks, and collection optimizations.
Proper configuration ensures reliable, secure and efficient collection of RA PLC data for the chosen methodology.
Uploading RA PLC Data to AWS
RA PLC data can be uploaded to AWS using various methods. The optimal approach depends on data types, volumes, frequency, network architecture, and use cases.
Different Methods for Uploading RA PLC Data to AWS
- S3 – Simple Storage Service offers highly scalable object storage.
- RDS – Relational Database Service provides relational databases.
- DynamoDB – Managed NoSQL database service.
- Kinesis – Real-time data streaming service.
- EMR – Elastic MapReduce for processing big data.
- SFTP – Secure FTP file transfer to S3 buckets.
- Direct Connect – Dedicated network connection from on-premise to AWS.
- IoT Platform – Handle uploads from many edge devices.
- Snowball Edge – Physical data transfer device to move local data to the cloud.
Choosing the Right Method for Your Needs
Factors that inform the selection of the best data upload method include:
- Data structure – Format such as SQL tables, NoSQL documents, log files, etc.
- Volume – Bandwidth capacity needed for upload size and frequency.
- Processing needs – Data preprocessing required before or after uploading.
- Security policies – Corporate policies guiding cloud data transfers.
- Network architecture – Makeup of existing IT infrastructure and integration requirements.
- Cost – Budget available for data transfer and storage costs.
- Compliance – Regulations applicable to the data being uploaded.
Analyzing these specifications indicates which approach or combination of methods works best.
Configuring the Data Upload Process
Steps to configure the chosen upload process effectively:
- Destination setup – Ensure target AWS service is provisioned and accessible.
- Security – Implement data encryption, VPNs, access controls, and other security measures.
- Network optimization – Increase bandwidth or use acceleration if needed to match data volumes.
- Software configuration – Set up and tune SDKs, drivers, and upload agents/apps for each direction.
- Testing and debugging – Thoroughly test transfers and troubleshoot any issues until smooth.
- Monitoring – Track upload rates, lags, failures, bottlenecks and other metrics.
- Alerting – Create alerts for problems requiring investigation or intervention.
- Maintenance – Perform software patches, updates, backups and equipment checks per schedule.
Properly configuring the entire workflow ensures seamless uploading of RA PLC data into the chosen AWS services.
Data Storage and Management
RA PLC data uploaded to AWS must be properly managed via data warehousing, databases, and other methods.
Different Options for Storing and Managing RA PLC Data in AWS
- S3 – Simple Storage Service provides scalable object storage using buckets.
- RDS – Relational Database Service manages SQL databases.
- DynamoDB – Managed NoSQL database for key-value store.
- Redshift – Data warehousing for large datasets and ETL.
- EMR – Elastic MapReduce manages big data processing.
- Glacier – Archival storage with high latency retrieval.
- Athena – Serverless querying against S3 data.
- OpsHub – Visualize relationships across siloed datasets.
- MSK – Managed streaming for Apache Kafka ingestion pipelines.
Choosing the Right Options for Your Needs
Key criteria for selecting AWS data storage and management include:
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- Query needs – Relational vs NoSQL vs Object data models.
- Retention policies – Short-term vs long-term storage requirements.
- Cost – Balance performance vs budget using tiered storage options.
- Scalability – Ability to handle spikes in data volumes as needs grow.
- Security – Built-in encryption, access controls, auditing capabilities.
- Durability – Data backup and redundancy levels.
- Latency – Speed of data retrieval and response time.
- Ease of use – complexity of administration and overhead costs.
Choose AWS data services based on the criticality, volumes, retention needs, and usage patterns of RA PLC data.
Configuring the Data Storage and Management Process
Steps to implement data management:
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- Select storage location(s) – Determine which combination of S3, RDS, DynamoDB, etc. based on data profile.
- Configure security – Set up robust user access, encryption, backup and recovery policies.
- Design database – For RDS and DynamoDB, design optimal schema and indexing for queries.
- Establish pipelines – Architect workflows for transforming, organizing, and loading data.
- Set retention rules – Define data expiration policies to migrate aged data to cheaper tiers.
- Create admin playbooks – Document policies, access control, maintenance, and procedures.
- Monitor usage – Track consumption, performance, scaling needs, and optimization opportunities.
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Following best practices for configuring data storage and management maximizes the value of RA PLC data on AWS.
Data Analysis and Visualization
Once securely stored in AWS, RA PLC data can drive major insights using analytics, business intelligence (BI), and data science techniques.
Different Tools and Services for Analyzing and Visualizing RA PLC Data in AWS
- QuickSight – Business intelligence service for dashboards and reporting.
- Athena – Serverless SQL querying for S3 data.
- EMR – Managed Hadoop cluster for big data analytics.
- Redshift – Data warehousing and ETL at scale.
- SageMaker – Build, train, and deploy machine learning models.
- Elasticsearch – Search and analyze structured and unstructured data.
- Kibana – Visualize Elasticsearch data with charts and graphs.
- Quicksight – Create interactive dashboards from a variety of data sources.
- Looker – Incorporate data analytics into applications.
Choosing the Right Tools and Services for Your Needs
Key selection criteria include:
- Skill level – Ease of use for SQL vs coding capabilities.
- Data structure – Tool compatibility with data formats – relational, geospatial etc.
- Visualization needs – Charts, graphs, heat maps, and other ways to represent data.
- Self-service – Ability for non-technical users to build queries and reports.
- Scalability – Large datasets, high concurrency, and cross-region needs.
- Cost – Pay only for the actual resources consumed for analysis.
- Security – IAM, VPC, encryption, data masking, and user access controls.
Align AWS analytics and visualization services with use cases, user profiles, and data types.
Configuring the Data Analysis and Visualization Process
Steps to implement AWS analytics:
- Select services – Choose QuickSight, Athena, EMR etc. based on criteria.
- Provision services – Configure the chosen AWS data analytics tools.
- Import data – Bring relevant datasets into the analytics environment.
- Model data – Structure and optimize data for best analysis performance.
- Develop metrics – Identify key performance indicators relevant to goals.
- Build dashboards – Design useful visualizations with charts, graphs, and geo maps.
- Establish self-service – Enable user-driven ad hoc querying without dependency on IT.
- Apply ML models – Plug in SageMaker models to unlock predictive insights.
Thoughtfully applying AWS analytics, BI and machine learning tools maximizes the value derived from RA PLC data.
Real-World Use Cases
Understanding how other organizations use AWS analytics on RA PLC data provides practical examples for application:
Examples of How RA PLC Data is Used in AWS
- OEE monitoring – Identify losses, inefficiencies, and bottlenecks in real-time production flows.
- Predictive maintenance – Analyze sensor data to detect anomalies and predict maintenance needs.
- Asset utilization – Optimize equipment use and scheduling based on runtime data.
- Quality analysis – Relate production metrics to defect rates and quality excursions.
- Energy management – Correlate energy consumption trends with equipment usage.
- Traceability – Link each product or process batch to related operations data for auditing.
- Simulation modeling – Apply EMR and SageMaker to improve designs and control logic.
How RA PLC Data Can Improve Efficiency, Productivity and Profitability
- Defect reduction – Adjust processes based on analytics insights to minimize quality issues.
- Waste minimization – Identify points of loss, idle time, or overprocessing for elimination.
- Throughput increase – Find production bottlenecks then improve asset utilization.
- Scrap reduction – Analyze root causes of scrap generation then prevent.
- Energy optimization – Adjust equipment operating parameters based on energy usage data correlations.
- Maintenance savings – Transition from preventive to predictive maintenance based on real performance data.
- Profitability lift – Boost margins by using RA PLC data to identify efficiency or revenue improvements.
Advanced analysis of RA PLC data uncovers opportunities for major operational gains.
Troubleshooting Tips
As with any complex IT/OT integration project, issues can arise when implementing the architecture to collect and upload RA PLC data to AWS.
Common Problems that Can Occur
- Insufficient bandwidth – Data transfer bottlenecks.
- High network latency – Data delays or timeouts.
- PLC connectivity issues – Communication failures between PLC and gateway.
- AWS service limits – Exceeding provisioned capacity limits.
- Protocol incompatibility – PLC tag and AWS data type mismatch.
- Incorrect data handling – Flawed parsing, formatting or manipulation.
- Security holes – Data exposure due to gaps in access controls or encryption.
- Cost overruns – Unanticipated charges on AWS monthly bill.
How to Troubleshoot Issues
- Increase logging – Add debug logging levels to see packet-level workflow.
- Inspect traffic – Use Wireshark to study raw two-way data flows.
- Review metrics – Analyze AWS CloudWatch metrics for abnormalities.
- Evaluate errors – Identify error patterns and research root causes online.
- Test permutations – Vary configurations and load levels to pinpoint problems.
- Simulate workflows – Model end-to-end transactions to find constraints.
- Enable alerts – Configure notifications for key performance thresholds.
- Contact support – Engage AWS Support or Slack user community for assistance.
Methodically isolating the root causes of problems leads to their resolution.
Summary
Migrating RA PLC data to AWS creates valuable new opportunities for industrial organizations to leverage cloud technologies. Following the steps outlined in this guide enables establishing the infrastructure to securely collect data from Allen-Bradley and ControlLogix PLCs using EtherNet/IP, transfer it efficiently to AWS, store it in managed databases, apply advanced analytics and machine learning to extract insights, and create dashboard visualizations. The benefits over keeping data in PLCs include greater accessibility, scalability, security, and broader analytical capabilities to ultimately drive operational improvements and strategic decisions through a data-driven approach. Careful planning and configuration avoids pitfalls to ensure a successful implementation. RA PLC data on AWS forms a foundation for leveraging cloud capabilities to maximize operational efficiency and intelligence.
FAQs
What are the different types of RA PLC data that can be collected?
RA PLCs contain control program values, production counts, equipment statuses, sensor measurements, alarms, events, histograms, trend data, logic flags, and controller diagnostics.
What are the different ways to collect RA PLC data?
Tag-based reads, message instructions, database queries, file access, explicit messaging, OPC UA, and leveraging PlantPAx are common PLC data collection approaches.
What are the different ways to upload RA PLC data to AWS?
Object storage in S3, streaming via Kinesis, relational databases like RDS, NoSQL databases like DynamoDB, SFTP transfers, Direct Connect, and Snowball devices are options.
What are the different options for storing and managing RA PLC data in AWS?
S3, RDS, DynamoDB, Redshift, EMR, Glacier, Athena, OpsHub, and MSK provide different object, file, relational, NoSQL, streaming, and big data management capabilities.
What are the different tools and services for analyzing and visualizing RA PLC data in AWS?
QuickSight, Athena, EMR, Redshift, SageMaker, Elasticsearch, Kibana, and Looker offer analytics, business intelligence, and machine learning capabilities.
What are some examples of how RA PLC data is being used in AWS?
OEE monitoring, predictive maintenance, asset utilization, quality optimization, energy management, traceability, and simulation modeling are common use cases.
