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This Guidance helps industrial energy customers derive near real-time visibility into early indications of equipment failure and equipment performance. This yields additional lead time to drive field action, such as equipment optimization and proactive maintenance planning. Customers can reduce unplanned downtime while optimizing equipment lifecycles, helping them improve maintenance planning and the associated costs.
Please note: [Disclaimer]
Architecture Diagram
Step 1
Unify unobstructed access to operational technology (OT) data by putting live measurements from SCADA systems and Amazon Monitron into Amazon Kinesis Data Streams.
Step 2
Hydrate your data lake using Amazon Managed Service for Apache Flink. Consume records from Kinesis Data Streams and Change Data Capture (CDC) streams from database engines. Write to popular data lake frameworks (such as HUDI and Delta).
Step 3
Simplify data lake management with three approaches. First, persist data with Amazon Simple Storage Service (Amazon S3). Next, add column- and row-level security to the AWS Lake Formation. Last, make information discoverable with AWS Glue.
Step 4
Create real-time insights with ready-to-use algorithms in Amazon Lookout for Equipment, and deploy custom machine learning models with Amazon SageMaker.
Step 5
Provide technicians with repair instructions from equipment manuals using Amazon Bedrock. Train machine learning models on Amazon EMR and package them for real-time inference.
Step 6
Display near real-time dashboards to users with Amazon Managed Grafana for timeseries data and Amazon QuickSight for business intelligence. Notify users about equipment health issues using Amazon Simple Notification Service (Amazon SNS).
Step 1
Unify unobstructed access to operational technology (OT) data by putting live measurements from SCADA systems and Amazon Monitron into Amazon Kinesis Data Streams.
Step 2
Hydrate your data lake using Amazon Managed Service for Apache Flink. Consume records from Kinesis Data Streams and Change Data Capture (CDC) streams from database engines. Write to popular data lake frameworks (such as HUDI and Delta).
Step 3
Simplify data lake management with three approaches. First, persist data with Amazon Simple Storage Service (Amazon S3). Next, add column- and row-level security to the AWS Lake Formation. Last, make information discoverable with AWS Glue.
Step 4
Create real-time insights with ready-to-use algorithms in Amazon Lookout for Equipment, and deploy custom machine learning models with Amazon SageMaker.
Step 5
Provide technicians with repair instructions from equipment manuals using Amazon Bedrock. Train machine learning models on Amazon EMR and package them for real-time inference.
Step 6
Display near real-time dashboards to users with Amazon Managed Grafana for timeseries data and Amazon QuickSight for business intelligence. Notify users about equipment health issues using Amazon Simple Notification Service (Amazon SNS).
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
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.
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Operational ExcellenceRead the Operational Excellence whitepaper
Amazon Managed Service for Apache Flink is capable of detecting schema changes within the sensor data ingested from sources such as Internet of Things (IoT) sensors and SCADA systems. Upon detecting such changes, Amazon SNS is used to send alerts. Promptly addressing these data source schema changes helps to mitigate unexpected behavior in downstream data pipelines.
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SecurityRead the Security whitepaper
AWS IoT Core facilitates secure communication and access control for IoT devices transmitting sensor data. Additionally, AWS Identity and Access Management (IAM) enables the management of permissions and access controls for the AWS resources utilized within this Guidance. These AWS services collectively protect both the data and resources from unauthorized access.
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ReliabilityRead the Reliability whitepaper
Kinesis Data Streams provides a durable and highly available data streaming service. Complementing this, Amazon Managed Service for Apache Flink offers a fault-tolerant stream processing system. These AWS services collectively contribute to the overall reliability of this Guidance through continuous data ingestion and processing, even in the face of potential failures.
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Performance EfficiencyRead the Performance Efficiency whitepaper
Amazon EMR Serverless furnishes a serverless environment suitable for executing big data analytics workloads, including the training of machine learning models. This AWS service enhances performance efficiency by automatically scaling resources to match the prevailing workload demand, thereby optimizing both resource utilization and overall performance.
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Cost OptimizationRead the Cost Optimization whitepaper
Amazon S3 provides cost-effective and scalable object storage to support the data lake of this Guidance. Using Amazon S3 for data storage contributes to cost optimization by eliminating the need to provision and manage storage infrastructure, allowing users to pay only for the storage capacity they consume.
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SustainabilityRead the Sustainability whitepaper
Amazon Managed Service for Apache Flink furnishes a managed stream processing service capable of automatically scaling resources in response to fluctuating demand. This AWS service contributes to sustainability by minimizing resource utilization during periods of low demand, thereby reducing the overall environmental impact of this Guidance.
Implementation Resources
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.