Modeling your industrial belongings at scale utilizing AWS IoT SiteWise

Introduction

Industrial and manufacturing prospects more and more depend on AWS IoT SiteWise to gather, retailer, set up, and monitor information from industrial gear at scale. AWS IoT SiteWise offers an industrial information basis for distant gear monitoring, efficiency monitoring, detecting irregular gear habits, and assist for superior analytics use circumstances.

Constructing equivalent to an information basis sometimes entails modeling your belongings and ingesting reside and historic telemetry information. This will likely require a major effort when addressing tens of hundreds of kit and ever-changing operations in pursuit of lowering waste and enhancing effectivity.

We launched three new options for AWS IoT SiteWise at re:Invent 2023 to enhance your asset modeling efforts. Clients can now symbolize gear elements utilizing Asset model components, selling reusability. With Metadata bulk operations, they’ll mannequin their gear and handle adjustments in bulk. User-defined unique identifiers assist prospects obtain consistency throughout the group through the use of their very own identifiers.

On this weblog put up, we are going to study 11 real-world buyer eventualities associated to asset modeling. We are going to share code examples that will help you study extra in regards to the new AWS IoT SiteWise options associated to every state of affairs.

Stipulations

1. Familiarity with asset modeling in AWS IoT SiteWise
2. An AWS account
3. Fundamental information of Python

Setup the setting

First, you’ll configure your developer workstation with AWS credentials and confirm that Python is put in. Subsequent you’ll set up Git, clone the code instance challenge to your workstation, and arrange the challenge. Lastly, you’ll create an AWS Identification and Entry Administration (IAM) coverage.

1. Create a Cloud9 environment utilizing Amazon Linux 2 platform (really useful) or use any on-premises machine as a developer workstation
2. Configure AWS credentials
3. Confirm Python 3.x is put in in your system by working python3 --version or python --version (on Home windows)
4. Utilizing terminal, set up Git and clone the Metadata Bulk Operations Sample for AWS IoT SiteWise repository from the AWS Samples library on Github

   sudo yum set up git
   git --version
   git clone https://github.com/aws-samples/metadata-bulk-operations-sample-for-aws-iot-sitewise.git

5. Set up required Python packages by working pip3 set up -r necessities.txt
6. Replace config/project_config.yml to supply required data for the job
   • s3_bucket_name: Title of the S3 bucket the place bulk definitions will likely be saved
   • job_name_prefix: Prefix for use for the majority operations jobs
7. Create an AWS Identification and Entry Administration (IAM) coverage with permissions that enable the change of AWS sources between Amazon S3, AWS IoT SiteWise, and your native machine. This may will let you carry out bulk operations.

Onboard and handle belongings at scale

AWS IoT SiteWise now supports the bulk import, export, and update of industrial equipment metadata for modeling at scale. These bulk operations are accessible via new API endpoints equivalent to CreateMetadataTransferJob, ListMetadataTransferJobs, GetMetadataTransferJob and CancelMetadataTransferJob.

With this new functionality, customers can bulk onboard and replace belongings and asset fashions in AWS IoT SiteWise. They will additionally migrate belongings and asset fashions between completely different AWS IoT SiteWise accounts.

You’ll primarily use metadata bulk import jobs for this weblog. The next diagram and steps clarify the workflow concerned in a metadata bulk import job.

Steps in Metadata Bulk Import Movement

1. Put together a job schema JSON file for AWS IoT SiteWise sources. This would come with asset fashions and belongings, following the AWS IoT SiteWise metadata transfer job schema. Add this file to an Amazon S3 bucket.
2. Make a metadata bulk import name to AWS IoT SiteWise, referencing the uploaded JSON file
3. AWS IoT SiteWise will import all of the sources specified within the JSON file
4. Upon completion, AWS IoT SiteWise will return the standing and a presigned Amazon S3 URL for any failures encountered
5. If there are failures, entry the offered report to research and perceive the basis trigger

You can even carry out bulk operations utilizing the console by navigating to Construct → Bulk Operations. Now that you simply perceive how metadata bulk operations work, you will notice how this function may help within the following real-world eventualities.

Situation 1 – Onboard preliminary asset fashions & belongings

Throughout a Proof of idea (POC), our prospects sometimes onboard a subset of their gear to AWS IoT SiteWise. Utilizing metadata bulk operations, you may import hundreds of asset fashions and belongings to AWS IoT SiteWise in a single import job.

For a fictitious automotive manufacturing firm, import asset fashions and belongings associated to the welding strains at one among its manufacturing crops.
python3 src/import/essential.py --bulk-definitions-file 1_onboard_models_assets.json

Situation 2 – Outline asset hierarchy

As soon as the asset fashions and belongings are created in AWS IoT SiteWise, you may outline the connection between belongings and create an asset hierarchy. This hierarchy helps customers to trace efficiency throughout completely different ranges, from the gear degree to the company degree.

Create an asset hierarchy for Sample_AnyCompany Motor manufacturing firm
python3 src/import/essential.py --bulk-definitions-file 2_define_asset_hierarchy.json

Situation 3 – Affiliate information streams with asset properties

Our prospects sometimes begin ingesting information from their information sources such OPC UA server, even earlier than modeling their belongings. In these conditions, the information ingested into SiteWise is saved in data streams that aren’t related to any asset properties. As soon as the ingestion train is full, you could affiliate the information streams with particular asset properties for contextualization.

Affiliate the information streams for Sample_Welding Robotic 1 and Sample_Welding Robotic 2 with corresponding asset properties.

python3 src/import/essential.py --bulk-definitions-file 3_associate_data_streams_with_assets.json

On this weblog, we created three separate metadata bulk import jobs. These jobs have been for creating asset fashions and belongings, defining the asset hierarchy, and associating information streams with asset properties. You can even carry out all of those actions utilizing a single metadata bulk import job.

Situation 4 – Onboard further belongings

After demonstrating the enterprise worth throughout POC, the subsequent step is to scale the answer inside and throughout crops. This scale can embody remaining belongings in the identical plant, and new belongings from different crops.

On this state of affairs, you’ll onboard further welding robots (#3 and #4), and a brand new manufacturing line (#2) from the identical Chicago plant.
python3 src/import/essential.py --bulk-definitions-file 4_onboard_additional_assets.json

Situation 5 – Create new properties

You may improve asset fashions to accommodate adjustments in information acquisition. For instance, when new sensors are put in to seize further information, you may replace the corresponding asset fashions to replicate these adjustments.

Add a brand new property Joint 1 Temperature to Sample_Welding Robotic asset mannequin
python3 src/import/essential.py --bulk-definitions-file 5_onboard_new_properties.json

Situation 6 – Repair handbook errors

Errors can happen throughout asset modeling particularly when customers manually enter data. Examples embody asset serial numbers, asset descriptions, and items of measurement. To appropriate these errors, you may replace the data with the proper particulars.

Right the serial variety of Sample_Welding Robotic 1 asset by changing the outdated serial quantity S1000 with S1001.
python3 src/import/essential.py --bulk-definitions-file 6_fix_incorrect_datastreams.json

Situation 7 – Relocate belongings

Manufacturing line operations change for a number of causes, equivalent to course of optimization, technological developments, and gear upkeep. Consequently, some gear might transfer from one manufacturing line to a different. Utilizing Metadata bulk operations, you may replace the asset hierarchy to adapt to the adjustments in line operations.

Transfer Sample_Welding Robotic 3 asset from Sample_Welding Line 1 to Sample_Welding Line 2.
python3 src/import/essential.py --bulk-definitions-file 7_relocate_assets.json

Situation 8 – Backup asset fashions and belongings

AWS recommends that you simply take common backups of asset fashions and belongings. These backups can be utilized for catastrophe restoration or to roll again to a previous model. To create a backup, you should utilize the bulk export operation. Whereas exporting, you may filter particular asset fashions and belongings to incorporate in your exported JSON file.

You’ll now again up the definitions of all welding robots beneath welding line 1. Exchange <YOUR_ASSET_ID> in 6_backup_models_assets.json with the Asset ID of Sample_Welding Line 1.

python3 src/export/essential.py --job-config-file 8_backup_models_assets.json

Situation 9 – Promote asset fashions and belongings to a different setting

Through the use of the metadata bulk export operation adopted by the majority import operation, you may promote a set of asset fashions and belongings from one setting to a different.

Promote all of the asset fashions and belongings from the event to the testing setting.
python3 src/import/essential.py --bulk-definitions-file 9_promote_to_another_environment.json

Keep consistency all through the group

Many industrial firms might have modeled some or most of their industrial gear in a number of methods equivalent to asset administration methods and information historians. It’s important for these firms to make use of widespread identifiers throughout the group to keep up consistency.

AWS IoT SiteWise now helps the usage of exterior ID and user-defined UUID for belongings and asset fashions. With the exterior ID function, customers can map their current identifiers with AWS IoT SiteWise UUIDs. You may work together with asset fashions and belongings utilizing these exterior IDs. The user-defined UUID function helps customers to reuse the identical UUID throughout completely different environments equivalent to growth, testing, and manufacturing.

To study in regards to the variations between exterior IDs and UUIDs, discuss with external IDs.

Situation 10 – Apply exterior identifiers

You may apply exterior IDs utilizing the AWS IoT SiteWise console, API, or metadata bulk import job. This may be achieved for current asset fashions, or belongings with none exterior IDs in AWS IoT SiteWise.

Apply exterior ID to an current asset, for instance, Sample_Welding Robotic 4.
python3 src/import/essential.py --bulk-definitions-file 10_apply_external_identifier.json

Promote standardization and reusability utilizing mannequin composition

AWS IoT SiteWise launched assist for a part mannequin. That is an asset mannequin sort that helps industrial firms mannequin smaller items of kit and reuse them throughout asset fashions. This helps standardize and reuse widespread gear elements, equivalent to motors.

For instance, a CNC Lathe (asset mannequin) is manufactured from elements equivalent to servo motors. With this function, a servo motor could be modeled independently as a part and reused in one other asset mannequin, equivalent to a CNC Machining Heart.

Situation 11 – Compose asset fashions

You may compose asset fashions utilizing the AWS IoT SiteWise console, API or metadata bulk import job.

Compose the Sample_Welding Robotic asset mannequin by independently modeling elements in a welding robotic, equivalent to a robotic joint.
python3 src/import/essential.py --bulk-definitions-file 11_compose_models.json

Clear Up

In the event you now not require the pattern resolution, take into account eradicating the sources.

Run the next to take away all of the asset fashions and belongings created utilizing this pattern repository.
python3 src/remove_sitewise_resources.py --asset-external-id External_Id_Company_AnyCompany

Conclusion

On this put up, we demonstrated the usage of new AWS IoT SiteWise options, equivalent to Metadata bulk operations, User-defined unique identifiers, and Asset model components. Collectively, these options promote standardization, reusability, and consistency throughout your group, whereas serving to you to scale and improve your asset modeling initiatives.

In regards to the authors

Raju Gottumukkala is a Senior WorldWide IIoT Specialist Options Architect at AWS, serving to industrial producers of their good manufacturing journey. Raju has helped main enterprises throughout the power, life sciences, and automotive industries enhance operational effectivity and income progress by unlocking true potential of IoT information. Previous to AWS, he labored for Siemens and co-founded dDriven, an Business 4.0 Information Platform firm.