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 knowledge from industrial tools at scale. AWS IoT SiteWise supplies an industrial knowledge basis for distant tools monitoring, efficiency monitoring, detecting irregular tools habits, and help for superior analytics use circumstances.

Constructing resembling an information basis sometimes entails modeling your belongings and ingesting dwell and historic telemetry knowledge. This may occasionally require a major effort when addressing tens of 1000’s of apparatus 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 characterize tools parts utilizing Asset mannequin parts, selling reusability. With Metadata bulk operations, they will mannequin their tools and handle adjustments in bulk. Consumer-defined distinctive identifiers assist prospects obtain consistency throughout the group by utilizing 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 can assist you study extra in regards to the new AWS IoT SiteWise options associated to every situation.

Conditions

1. Familiarity with asset modeling in AWS IoT SiteWise
2. An AWS account
3. Primary data 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 undertaking to your workstation, and arrange the undertaking. Lastly, you’ll create an AWS Identification and Entry Administration (IAM) coverage.

1. Create a Cloud9 setting utilizing Amazon Linux 2 platform (beneficial) 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 operating python3 --version or python --version (on Home windows)
4. Utilizing terminal, set up Git and clone the Metadata Bulk Operations Pattern 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 operating 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 probably 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 permit the alternate of AWS assets between Amazon S3, AWS IoT SiteWise, and your native machine. This may mean you can carry out bulk operations.

Onboard and handle belongings at scale

AWS IoT SiteWise now helps the majority import, export, and replace of commercial tools metadata for modeling at scale. These bulk operations are accessible by means of new API endpoints resembling CreateMetadataTransferJob, ListMetadataTransferJobs, GetMetadataTransferJob and CancelMetadataTransferJob.

With this new functionality, customers can bulk onboard and replace belongings and asset fashions in AWS IoT SiteWise. They’ll additionally migrate belongings and asset fashions between totally 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 Circulation

1. Put together a job schema JSON file for AWS IoT SiteWise assets. This would come with asset fashions and belongings, following the AWS IoT SiteWise metadata switch 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 assets 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 supplied report to research and perceive the basis trigger

You can too 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 characteristic will help within the following real-world eventualities.

State of affairs 1 – Onboard preliminary asset fashions & belongings

Throughout a Proof of idea (POC), our prospects sometimes onboard a subset of their tools to AWS IoT SiteWise. Utilizing metadata bulk operations, you’ll be able to import 1000’s 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 in every of its manufacturing vegetation.
python3 src/import/major.py --bulk-definitions-file 1_onboard_models_assets.json

State of affairs 2 – Outline asset hierarchy

As soon as the asset fashions and belongings are created in AWS IoT SiteWise, you’ll be able to outline the connection between belongings and create an asset hierarchy. This hierarchy helps customers to trace efficiency throughout totally different ranges, from the tools stage to the company stage.

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

State of affairs 3 – Affiliate knowledge streams with asset properties

Our prospects sometimes begin ingesting knowledge from their knowledge sources such OPC UA server, even earlier than modeling their belongings. In these conditions, the information ingested into SiteWise is saved in knowledge streams that aren’t related to any asset properties. As soon as the ingestion train is full, it’s essential to 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/major.py --bulk-definitions-file 3_associate_data_streams_with_assets.json

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

State of affairs 4 – Onboard further belongings

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

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

State of affairs 5 – Create new properties

You’ll be able to improve asset fashions to accommodate adjustments in knowledge acquisition. For instance, when new sensors are put in to seize further knowledge, you’ll be able to 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/major.py --bulk-definitions-file 5_onboard_new_properties.json

State of affairs 6 – Repair handbook errors

Errors can happen throughout asset modeling particularly when customers manually enter data. Examples embrace asset serial numbers, asset descriptions, and items of measurement. To appropriate these errors, you’ll be able to replace the knowledge with the right particulars.

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

State of affairs 7 – Relocate belongings

Manufacturing line operations change for a number of causes, resembling course of optimization, technological developments, and tools upkeep. In consequence, some tools could transfer from one manufacturing line to a different. Utilizing Metadata bulk operations, you’ll be able to 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/major.py --bulk-definitions-file 7_relocate_assets.json

State of affairs 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 need to use the bulk export operation. Whereas exporting, you’ll be able to filter particular asset fashions and belongings to incorporate in your exported JSON file.

You’ll now again up the definitions of all welding robots underneath 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/major.py --job-config-file 8_backup_models_assets.json

State of affairs 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’ll be able to 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/major.py --bulk-definitions-file 9_promote_to_another_environment.json

Preserve consistency all through the group

Many industrial firms could have modeled some or most of their industrial tools in a number of programs resembling asset administration programs and knowledge historians. It’s important for these firms to make use of widespread identifiers throughout the group to take care of consistency.

AWS IoT SiteWise now helps using exterior ID and user-defined UUID for belongings and asset fashions. With the exterior ID characteristic, customers can map their current identifiers with AWS IoT SiteWise UUIDs. You’ll be able to work together with asset fashions and belongings utilizing these exterior IDs. The user-defined UUID characteristic helps customers to reuse the identical UUID throughout totally different environments resembling improvement, testing, and manufacturing.

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

State of affairs 10 – Apply exterior identifiers

You’ll be able to 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/major.py --bulk-definitions-file 10_apply_external_identifier.json

Promote standardization and reusability utilizing mannequin composition

AWS IoT SiteWise launched help for a part mannequin. That is an asset mannequin sort that helps industrial firms mannequin smaller items of apparatus and reuse them throughout asset fashions. This helps standardize and reuse widespread tools parts, resembling motors.

For instance, a CNC Lathe (asset mannequin) is fabricated from parts resembling servo motors. With this characteristic, a servo motor might be modeled independently as a part and reused in one other asset mannequin, resembling a CNC Machining Middle.

State of affairs 11 – Compose asset fashions

You’ll be able to 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 parts in a welding robotic, resembling a robotic joint.
python3 src/import/major.py --bulk-definitions-file 11_compose_models.json

Clear Up

If you happen to now not require the pattern answer, think about eradicating the assets.

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 using new AWS IoT SiteWise options, resembling Metadata bulk operations, Consumer-defined distinctive identifiers, and Asset mannequin parts. Collectively, these options promote standardization, reusability, and consistency throughout your group, whereas serving to you to scale and improve your asset modeling initiatives.

Concerning 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 knowledge. Previous to AWS, he labored for Siemens and co-founded dDriven, an Business 4.0 Information Platform firm.