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AWS Solutions Library

Guidance for Amazon Lookout for Equipment Integration with SAP Plant Maintenance

Overview

This Guidance shows how you can improve failure predictions for large equipment and complex assemblies without requiring supervised machine learning (ML). Amazon Lookout for Equipment analyzes data from your sensors and systems—such as pressure, flow rate, revolutions per minute, temperature, and power—to automatically train a model specific to your equipment. This unique model can then identify early warning signs, pinpoint issues, and predict the magnitude of impact of a detected event. Your SAP system then creates maintenance notifications or measurement readings to help you proactively address the predicted event.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon CloudWatch lets you independently monitor each architecture layer and component for key performance indicators, with automated resolution for each managed service. This observability helps you focus on meaningful data to understand your workload's interactions and output.

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All the resources in this Guidance run within a VPC so that you can keep them separate from other resources. And, you can encrypt data at rest using AWS server-side keys. Additionally, Secrets Manager provides key rotation policies that you can use to store your SAP authentication information.

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Amazon S3, DynamoDB, and Lambda are serverless services and scale horizontally, automatically responding to the velocity of data ingestion and processing. The use of serverless services across multiple Availability Zones provides redundancy in the event of a failure in any single Availability Zone.

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Lookout for Equipment helps you detect abnormal equipment behavior by analyzing sensor data so that you can avoid unplanned downtime. With this service, you can accurately monitor sensor data and analyze historical maintenance trends to support workloads that are more scalable and efficient.

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This Guidance uses highly available and serverless services like Amazon S3, DynamoDB, and Lambda, so you only pay for the resources you use. Features like Lambda warm starts and Amazon S3 Intelligent-Tiering help minimize compute and storage requirements and costs. You can further optimize costs by extracting only the business data group that you need and minimizing the number of implemented flows based on the granularity of your reporting needs.

Read the Cost Optimization whitepaper 

Serverless services maximize overall resource utilization by only using compute when it is needed, reducing the overall energy required to operate your workloads. You can also use the AWS Billing Conductor carbon footprint tool to calculate and track the environmental impact of your workloads over time at the account, AWS Region, and service levels.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.