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AWS Solutions Library

Guidance for Equipment Analytics on AWS

Overview

This Guidance illustrates a comprehensive, three-modular approach to connecting and analyzing your industrial equipment, addressing the key stages of equipment connectivity, analytics, and data visualization. It includes an edge location component that enables on-site data ingestion, capturing critical parameters like temperature, vibration, and pressure. The next module shows how to ingest and process real-time data from the edge. It prepares and streams the data to the centralized data lake, where artificial intelligence and machine learning (AI/ML) services use the data to detect abnormal equipment behavior, predict potential failures, and avoid unplanned downtime. Finally, the data visualization module shows how data is ingested from the data lake to provide dashboards and 3D visualizations and enable direct data exploration and analysis.

How it works

Overview

The architecture diagrams for this Guidance are comprised of a suite of three modules: A) Data ingestion, B) Data streaming, processing & machine learning, and C) Data visualization and notifications. This diagram provides a conceptual overview of each module and its interdependencies.

Download the architecture diagram
Architecture overview diagram illustrating how equipment and IoT sensor data from an industrial plant or facility is streamed to AWS Cloud, stored in a data lake, analyzed with AI/ML, and visualized for users and applications.

Data ingestion

This architecture diagram displays an edge location component that enables on-site data ingestion from IoT sessions, PLCs, SCADA, and historians.

Download the architecture diagram
Architecture diagram showing the AWS equipment analytics data ingestion workflow for an industrial plant or facility. The diagram illustrates the flow from PLC, SCADA, historians, IoT sensors, and Amazon Monitron, through AWS IoT Greengrass Edge Gateway with partner connectors and AWS IoT SiteWise Edge, to downstream applications such as machine learning inference, MQTT broker, and custom applications.

Data streaming, processing, and machine learning

This architecture diagram shows how data from the edge location is processed and ingested into a data lake, along with AI/ML services.

Download the architecture diagram
Architecture diagram illustrating data streaming, industrial data lake, and AI/ML services for equipment analytics using AWS IoT SiteWise, Amazon S3, AWS Glue, AWS Lake Formation, Amazon Athena, Amazon Lookout for Equipment, Amazon Monitron, Amazon SageMaker, and Amazon Bedrock.

Data visualization and notifications

This architecture diagram shows how data is ingested and used for dashboards for 3D visualizations.

Download the architecture diagram
Architecture diagram illustrating AWS equipment analytics with data visualization and notification flows using AWS IoT TwinMaker, AWS IoT SiteWise, Industrial Data Lake, Amazon Managed Grafana, Amazon SNS, and AWS IoT Events, highlighting interaction with operations and maintenance personnel.

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.

The services used in this Guidance aid in data ingestion, local processing, event monitoring, message delivery, and machine learning for manufacturing optimization. For example, AWS IoT SiteWise and IoT Greengrass handle data collection, processing, and edge computing, while AWS IoT Events identifies significant sensor data events. Amazon SNS manages messaging, and AI/ML is used to extract insights from operational data for process improvements, predictive maintenance, quality control, and automated inspections.

Read the Operational Excellence whitepaper

This Guidance uses AWS Identity and Access Management (IAM) and AWS IoT Core to help protect your data, systems, and assets while improving your security posture. For instance, IAM policies are scoped to allow only the minimum required permissions, while IoT Greengrass policies control which devices can send data and interact with the cloud, limiting unauthorized access to prevent accrued charges, tampering, or malicious operations. Additionally, encryption at rest is enabled for all cloud data destinations, like Amazon Simple Storage Service (Amazon S3) and AWS IoT SiteWise. By implementing granular access controls, device policies, and data encryption, these services enhance security by mitigating the risks of unauthorized access, data breaches, and potential threats to industrial assets and operations.

Read the Security whitepaper

With AWS IoT SiteWise and AWS IoT TwinMaker, throttling limits are in place for data ingress and egress to help assure continued operation. In addition, the Managed Grafana console provides access to a workspace for visualizing and analyzing metrics, logs, and traces without the need to manage infrastructure. It automatically provisions, configures, manages operations, and scales to meet dynamic usage demands—critical for handling peak industrial operations.

For optimal performance, it is recommended that you configure a multi-Availability Zone (AZ) deployment strategy.

By implementing a multi-AZ architecture, throttling safeguards, and managed services that auto-scale, this Guidance helps ensure reliable data ingestion, analysis, and visualization even during high-traffic periods, minimizing downtime and disruptions to industrial processes.

Read the Reliability whitepaper

AWS IoT TwinMaker collects outgoing messages to AWS IoT SiteWise to prevent throttling as tag volumes increase. For analytics, AWS IoT SiteWise and Amazon S3 automatically scale to accommodate data storage needs. For edge applications, AWS IoT SiteWise Edge offers features like edge dashboards and processing optimized for near real-time performance.

In addition, this Guidance uses the auto-scaling capabilities of AWS IoT SiteWise and Amazon S3. It also optimizes the localized processing capability for AWS IoT SiteWise Edge to deliver high performance across data ingestion, storage, analytics, and edge computing. This scalable and optimized approach prevents throttling, accommodates fluctuating data volumes, and provides low-latency processing for time-sensitive industrial operations.

Read the Performance Efficiency whitepaper

For equipment connectivity, the main cost savings come from reduced onsite operational effort, such as regulator costs and human resources. Therefore, you should choose services that bring automation, minimum administration, and scalability. The engineering hours saved should be the primary metric when assessing AWS technologies, as service rates are a smaller part of the financial assessment. Additionally, the total cost of ownership (TCO) and overhead savings are important, as AWS customers often spend significant resources managing on-premises IT infrastructure.

AWS IoT SiteWise and AWS IoT TwinMaker are cost-optimized, managed services that provide digital twin capabilities at the lowest possible price point. Their pay-as-you-go pricing model charges only for the data ingested, stored, and queried. AWS IoT SiteWise also offers optimized storage settings to move data from hot to cold tiers in Amazon S3.

Read the Cost Optimization whitepaper

To address sustainability commitments across industries like manufacturing, construction, energy, utilities, and oil and gas, this Guidance uses AWS IoT SiteWise and Amazon Bedrock. These services enhance sustainability through an elastic IT infrastructure that scales based on usage, minimizing excess compute resources and associated emissions that can be tracked through the Customer Carbon Footprint Tool. Additionally, these services enable engineering agility with digital twins, event-based automation, and AI/ML insights to optimize on-site operations, increase efficiency, and minimize emissions.

AWS IoT SiteWise further supports sustainability with its cold storage tier using the open-source, columnar Apache Parquet format on Amazon S3 for efficient data storage and retrieval.

Lastly, Amazon Bedrock provides data visualization and natural language processing capabilities, where you have access to foundation models to build generative AI applications. These capabilities help you identify unknown risk areas from historical data and validate the effectiveness of interventions, further aiding emission reduction efforts.

Read the Sustainability whitepaper

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.