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This Guidance shows how to set up the data flow and AWS service integration for Nanoprecise MachineDoctor. By offering real-time predictive information about the health and performance of industrial assets, Nanoprecise solutions help you extend the useful life of assets, reduce maintenance costs, facilitate root cause failure analysis, and increase the mean time between failures. By running MachineDoctor on AWS, you can also simplify sensor implementation and enhance existing systems. Additionally, by using the integrated analytics applications on AWS, your process engineers can make more informed decisions about critical assets to improve maintenance strategies and reduce equipment downtime.
Please note: [Disclaimer]
Architecture Diagram
[Architecture diagram description]
Step 1
Installed on industrial assets, MachineDoctor wireless battery-operated edge sensors measure and collect data for six parameters: triaxial vibration, acoustic emission, surface temperature, humidity, true RPM, and magnetic flux. They also perform anomaly detection at the edge.
Step 2
Edge sensors send their telemetry data to the tenant-specific Nanoprecise Predictive Maintenance SaaS solution through an Agent cloud on a predefined schedule and when anomalies are detected. This solution aligns with the System and Organization Controls (SOC) 2 Type II examination.
Step 3
Existing industrial systems, such as Supervisory Control and Data Acquisition (SCADA), data historians, Programmable Logic Controllers (PLCs), and third-party sensors, can also send their data to the Nanoprecise platform through open APIs.
Step 4
Data is analyzed by Nanoprecise RotationLF using statistical and physics-based models for anomaly detection, fault identification, fault characterization, potential causes and suggested actions, estimated energy loss, and estimated remaining useful life. NrgMonitor uses the telemetry data to track power consumption and CO2 emissions, enabling energy-centered maintenance.
Step 5
The Nanoprecise SaaS platform provides an open API that allows you to integrate telemetry data and insights with your existing enterprise applications, such as computerized maintenance management systems (CMMS), data historians, and enterprise resource planning (ERP) systems.
Step 6
You can monitor your industrial assets and access the insights generated through pre-built dashboards available on the Nanoprecise SaaS platform. You can be alerted with just-in-time notifications and alerts through email and/or SMS.
Step 7
Export telemetry data and insights into your own cloud environment using AWS Lambda. Store the data in Amazon Simple Storage Service (Amazon S3).
Hydrate the industrial data lake with asset maintenance data to use and train machine learning models on Amazon SageMaker. Query the data in Amazon S3 with Amazon Athena. Visualize, analyze, and gain business insights on your data with Amazon Managed Grafana and Amazon QuickSight.
Step 1
Installed on industrial assets, MachineDoctor wireless battery-operated edge sensors measure and collect data for six parameters: triaxial vibration, acoustic emission, surface temperature, humidity, true RPM, and magnetic flux. They also perform anomaly detection at the edge.
Step 2
Edge sensors send their telemetry data to the tenant-specific Nanoprecise Predictive Maintenance SaaS solution through an Agent cloud on a predefined schedule and when anomalies are detected. This solution aligns with the System and Organization Controls (SOC) 2 Type II examination.
Step 3
Existing industrial systems, such as Supervisory Control and Data Acquisition (SCADA), data historians, Programmable Logic Controllers (PLCs), and third-party sensors, can also send their data to the Nanoprecise platform through open APIs.
Step 4
Data is analyzed by Nanoprecise RotationLF using statistical and physics-based models for anomaly detection, fault identification, fault characterization, potential causes and suggested actions, estimated energy loss, and estimated remaining useful life. NrgMonitor uses the telemetry data to track power consumption and CO2 emissions, enabling energy-centered maintenance.
Step 5
The Nanoprecise SaaS platform provides an open API that allows you to integrate telemetry data and insights with your existing enterprise applications, such as computerized maintenance management systems (CMMS), data historians, and enterprise resource planning (ERP) systems.
Step 6
You can monitor your industrial assets and access the insights generated through pre-built dashboards available on the Nanoprecise SaaS platform. You can be alerted with just-in-time notifications and alerts through email and/or SMS.
Step 7
Export telemetry data and insights into your own cloud environment using AWS Lambda. Store the data in Amazon Simple Storage Service (Amazon S3).
Hydrate the industrial data lake with asset maintenance data to use and train machine learning models on Amazon SageMaker. Query the data in Amazon S3 with Amazon Athena. Visualize, analyze, and gain business insights on your data with Amazon Managed Grafana and Amazon QuickSight.
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
Nanoprecise manages the predictive maintenance SaaS solution, using Elasticsearch to analyze logs, track usage, and perform preventive maintenance. When data is exported to your AWS account using Lambda, pre-built logging, monitoring, and dashboards provide additional operational insights. Serverless analytics services are recommended to abstract away operational tasks, like backups, patching, and scaling, because the infrastructure is handled by AWS.
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SecurityRead the Security whitepaper
The Nanoprecise solution has completed a SOC 2 Type 2 examination, validating security, availability, processing integrity, confidentiality, and privacy controls. Data is encrypted in transit and at rest. The MachineDoctor sensors connect to the Nanoprecise Agent Cloud for authentication and authorization of Internet of Things (IoT) devices, including certificate rotation, and secure them in tenant-specific queues. If data is exported to your account, Lambda accesses it over TLS, and Amazon S3 offers client-side and server-side encryption options for data confidentiality.
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ReliabilityRead the Reliability whitepaper
Nanoprecise manages the availability and reliability of the SaaS application using a highly available network topology. The MachineDoctor sensors are wireless, have low battery consumption with energy harvesting, are IP68 waterproof, and are also certified for explosive and hazardous environments, supporting asset uptime.
Exporting data to your account using serverless services like Lambda and Amazon S3 aligns with reliability best practices, using the built-in distributed, fault-tolerant, and highly available nature of these managed services. This reduces your operational burden and failure risk when compared to managing infrastructure directly.
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Performance EfficiencyRead the Performance Efficiency whitepaper
In this Guidance, Nanoprecise is responsible for the performance of the SaaS solution. The MachineDoctor sensors can have 4G/LTE communication for remote connectivity. When exporting data to the AWS account, serverless and managed services like Lambda, Athena, and SageMaker are used to automatically scale resources based on demand for optimal performance and efficiency without manual infrastructure management.
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Cost OptimizationRead the Cost Optimization whitepaper
The Nanoprecise solution is priced by sensor, providing both hardware and a SaaS platform, allowing you to pay only for the assets being monitored. Nanoprecise also offers a Money Back Guarantee (MBG) initiative designed to deliver a quantifiable return on investment (ROI). You can get up to 70% back on your subscription price if the promised ROI is not achieved within the first year, subject to eligibility.
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SustainabilityRead the Sustainability whitepaper
In this Guidance, Nanoprecise manages the resources for the SaaS application to achieve optimum usage. Nanoprecise also monitors the change in the power consumption of various components and equipment in near real-time from the data collected through MachineDoctor. The insights allow maintenance teams to take corrective steps to reduce their carbon footprint.
Using serverless and managed services like Lambda, Amazon S3, and Athena aligns with sustainability by improving resource utilization and reducing the environmental impact compared to provisioning infrastructure directly.
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.
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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.
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.