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AWS Solutions Library

Guidance for Asset Maintenance and Reliability with Nanoprecise MachineDoctor on AWS

Overview

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.

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.

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.

Read the Operational Excellence 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.

Read the Security 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.

Read the Reliability 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.

Read the Performance Efficiency 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.

Read the Cost Optimization 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.

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.

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