Building Smart Industrial Machines with AWS: A Comprehensive Guide

Introduction

In today’s competitive industrial landscape, manufacturers of industrial machines such as wind turbines, robots, and mining machinery are constantly seeking innovative ways to maximize the potential of their products. By connecting these machines, they gain unprecedented visibility, unlock new revenue streams, and deliver enhanced services to their customers, making their operations and machines smarter. However, building a comprehensive machine-to-cloud connected solution from scratch can be a complex and time-consuming endeavor. It requires building local compute capabilities, collecting and ingesting data, cataloging and transforming it in real-time, developing access interfaces, and performing advanced analytics to enable AI, machine learning, and generative AI use cases. This is where AWS IoT managed services come in. AWS’s suite of Internet of Things (IoT) and Artificial Intelligence (AI) services are specifically designed to help industrial equipment manufacturers rapidly develop smart, secure, and scalable solutions—without the need to invest heavily in complex infrastructure and engineering. By leveraging AWS’s robust infrastructure and advanced technologies, manufacturers can streamline operations, gain deeper insights through data analysis, and implement cutting-edge machine learning solutions. This not only allows them to focus on designing and producing high-quality products but also enables them to enhance product functionality over time, provide additional services, and create new revenue streams. All of this is achieved while AWS handles the complexities of technology management and scalability with its reliable and secure platform. In this blog post, we’ll explore how AWS IoT managed services can accelerate your transformation into a smart industrial leader and share best practices from a variety of AWS IoT customers.

Challenges in Building, Deploying and Maintaining Smart Industrial Machines

For industrial machine manufacturers, the path to becoming a smart, connected industrial machine manufacturer is paved with significant challenges. Leading companies in this space possess deep expertise in their products and domains, but sometimes lack the in-house capabilities to deploy complex edge computing and cloud-based applications at scale and at speed. Coordinating the logistics of connecting thousands of high-value industrial machines, maintaining adequate cybersecurity standards, and managing the overall cost of ownership can quickly become overwhelming. As a result, industrial machine manufacturers often find themselves spending more time and resources on undifferentiated heavy lifting, rather than focusing on core business innovation. Industrial equipment users expect their machinery to be smarter, more efficient, and capable of delivering new digital services. To stay competitive, industrial machine manufacturers must be able to rapidly develop and deploy these new capabilities, while reducing the resources required to maintain these industrial machines, such as the cost and time required to develop software, run quality assurances processes, monitor and operate IT infrastructure, etc. However, building the necessary technology foundation from scratch can significantly slow down time-to-market and hinder their ability to respond to evolving market demands. Industrial leaders need proven, scalable, and cost-effective solutions that enable them to swiftly develop and deploy smart, connected machines leveraging new AI/ML capabilities, all while maintaining their focus on core product innovation and delivering customer value.

Accelerating Innovation with AWS IoT Managed Services

Building and maintaining a solution from the ground up is no longer required for any industrial machine manufacturer. Companies that are just starting their digital transformation and those that have already begun their smart machine journey can benefit from AWS IoT managed services. By leveraging these services, manufacturers can focus their resources on business innovation, reduce costs, and accelerate time to market. Instead of building the technological foundation from scratch, all companies can utilize APIs provided by AWS’s managed services to meet their equipment data processing and device management needs. This allows them to concentrate on their core competencies, such as acquiring new customers and creating new revenue streams, while developing solutions more quickly and cost-effectively. Moreover, companies that have already implemented IoT solutions can further simplify the maintenance and costs of their systems and enhance their digital offerings by integrating advanced capabilities like digital twins and AI/ML.

Comprehensive AWS IoT Integration

Connecting industrial machines to the cloud requires seamlessly integrating various technologies, including secure device connectivity, remote management, and advanced data processing and analytics. The AWS portfolio of IoT services offers comprehensive, end-to-end capabilities that address these challenges, enabling industrial machine manufacturers to build and maintain smart, edge to cloud connected machines quickly and efficiently. These capabilities can also help manufacturers leverage industrial data within their industrial machines for creating new services and revenues streams.

AWS IoT Core, a managed service that provides secure, bi-directional communication between industrial equipment and the cloud, acts as the gatekeeper between industrial machines and the AWS cloud. AWS IoT Core ensures secure reception and processing of data transmitted from devices as it arrives. The service supports MQTT, HTTPS and MQTT over WebSocket to ensure reliable, always-on connectivity, while also handling critical identity and message routing functionalities.

Telemetry data from connected industrial machines available in AWS IoT Core, or data originating directly from industrial machines, can be easily ingested and processed using AWS IoT SiteWise. This purpose-built service for the industrial sector streamlines data collection and analysis, enabling manufacturers to gain valuable insights and optimize the operations of their smart products.

AWS IoT SiteWise not only collects and stores time-series data but also provides advanced edge and cloud capabilities for contextualizing, modeling, and accessing this data through flexible interfaces and pre-built integrations with other AWS services. These integrations include AWS IoT TwinMaker, which simplifies the creation of digital twins for real-world systems, and Amazon Lookout for Equipment, which automatically detects abnormal equipment behavior to support predictive maintenance and reduce downtime. With these pre-built integrations and flexible APIs, industrial organizations can gain valuable insights without needing to handle complex integration tasks themselves.

To enhance the security of industrial machines, AWS IoT Device Defender can regularly audit your fleet for compliance with security best practices, identifies unusual behavior, and notifies you of potential issues, thereby providing a robust security framework that addresses a common concern for manufacturers of industrial machines.

Finally, the total cost of ownership is controlled through the use of managed services. By leveraging AWS’s portfolio of IoT services, industrial manufacturers can reduce the need for complex in-house IT teams to develop and maintain the digital infrastructure supporting their smart industrial machines. This enables them to allocate resources more efficiently, focusing on core product innovation for market differentiation and enhancing customer value, rather than managing routine IT tasks.

Overview of AWS Architecture Guidance for Smart Industrial Machines

In the modern industrial landscape, leveraging advanced technologies to enhance operational efficiency and product innovation is crucial. The diagram below illustrates a comprehensive architecture for smart industrial machines using AWS IoT services. Starting from secure device connectivity and edge computing to robust data management and advanced analytics, this architecture integrates various AWS IoT services to provide a scalable, secure, and efficient solution. It showcases how industrial equipment of machine builders can connect to the cloud, manage data, ensure security, and utilize AI/ML capabilities, thereby enabling these manufacturers to focus on core innovations for their products and on delivering customer value, while AWS handles the complex technological infrastructure.

Figure 1 – Connect and manage Smart Industrial Machines

1. An industrial machine can connect to AWS IoT Core using various edge software options, such as the managed edge runtime provided by AWS IoT Greengrass, any MQTT-compliant client, or the AWS IoT Device SDK. Telemetry data is seamlessly ingested into any backend as soon as it becomes available in AWS IoT Core and can be directly routed to AWS IoT SiteWise using IoT Core rules. Additionally, AWS IoT SiteWise offers a REST API for direct data ingestion into the service.
2. AWS IoT SiteWise offers ingestion, real-time data processing, advanced data storage, and robust data access capabilities. For deployed industrial machines that lack direct internet connectivity, an edge gateway can manage running processes, connectivity, and local data processing. The edge gateway collects data from industrial machines, then processes, stores, and forwards it cost-effectively to AWS IoT SiteWise while being managed remotely using AWS IoT SiteWise Edge, an edge component that runs on AWS IoT Greengrass. Additionally, you can leverage this managed runtime to deploy extra components at the edge to support local processing or AI/ML inference.
3. AWS IoT Core provides a secure way to connect industrial machines to the cloud. This managed service includes identity & access management, message brokering, and message routing functionality, all supported by always-on, two-way communication via the MQTT protocol over TCP or over WebSocket. Additionally, the service supports HTTPS for message publishing.
4. Remotely provision, monitor, update, and troubleshoot industrial machines or gateways at scale by leveraging AWS IoT Device Management. This service enables users to upload and view device information and configuration, organize their device inventory, monitor their fleet of devices, troubleshoot individual devices, and remotely manage devices deployed across various locations, including over-the-air (OTA) software updates.
5. AWS IoT Device Defender audits your fleet for compliance with security best practices, continuously monitors the fleet, detects abnormal behavior, and alerts you to any security findings. These findings are also sent to AWS Security Hub, providing a centralized view of all security issues across various AWS services.
6. Ingest and contextualize operational data from industrial machines using AWS IoT SiteWise through data streams, asset models, and an asset catalog. Leverage the platform to compute performance metrics, store time-series data across three available storage tiers, and define alarms. The service offers flexible data access for external applications through multiple interfaces, including hot and warm storage on Amazon S3, a SQL-like query interface, a user-friendly API, and property notifications to seamlessly publish machine data updates to AWS IoT Core.

Figure 2 – Build an industrial data foundation for Smart Industrial Machines

7. Build an industrial data lake using the contextual data provided by AWS IoT SiteWise. Govern, secure, and share this data with AWS Lake Formation for advanced analytics. Catalogue and analyze the data using AWS analytics services such as AWS Glue and Amazon Athena.
8. Remotely monitor industrial machines in near real-time using AWS IoT SiteWise Monitor or Amazon Managed Grafana to create rich, contextual dashboards. Build digital twins with AWS IoT TwinMaker, or develop custom applications using your preferred framework, including AWS Amplify, which leverages the AWS IoT Application Kit.
9. Detect anomalies using advanced alarm thresholds and notify operational personnel about machine health with AWS IoT Events and Amazon SNS. Additionally, create state machines and complex event monitoring applications by leveraging detector models in AWS IoT Events.
10. Develop custom AI/ML solutions with services like AWS SageMaker and Amazon Bedrock. Additionally, leverage Amazon Lookout for Vision to detect defects using computer vision.
11. Build a cloud data warehouse to power data-driven decisions and generate insights using Amazon QuickSight or your preferred BI tool. With the Amazon Q add-on for Amazon QuickSight, business users can ask questions in natural language and receive insights within seconds. Additionally, empower enterprise users with A and Amazon Q Business, a generative AI-powered enterprise assistant that can answer questions and securely complete tasks based on data from enterprise systems.
12. Provide historical and near real-time product data to customers by building serverless APIs using Amazon API Gateway and AWS AppSync that can scale to millions of users.
13. Utilize Amazon DynamoDB for configuration management, Amazon S3 for artifact storage, AWS CodePipeline for automating CI/CD processes, and AWS IoT Greengrass for edge device life cycle management. By integrating these services, you can effectively streamline the deployment, management, and updates of both cloud and edge applications.
14. Use Amazon Connect to meet customer servicing needs and to empower agents with contextual product information and suggestions for faster resolution of issues.

Industrial Leaders Use AWS IoT

Industrial machine manufacturers worldwide are using AWS IoT and AI managed services to build faster, better, and more secure industrial smart products, leveraging the edge and cloud capabilities of AWS and its partners. For example, some of these manufacturers include Amazon Robotics, Heidelberger Druckmaschinen AG (HEIDELBERG), Deere, Philips, Kraus Maffei, ENVEA, Martin Engineering, KEMPPI, Techno Brazing, Pentair, and more. You can read below the highlights of four leading machine makers that work with AWS IoT. To find out all the details, read the full story.

1. KONE, a global leader in the elevator and escalator industry, faced the challenge of connecting to the cloud all 1.6 million pieces of equipment in KONE’s maintenance base for enhanced remote monitoring and maintenance. They solved this by leveraging AWS IoT Core, AWS IoT Device Management and AWS IoT Twin Maker to build a scalable and reliable IoT platform. This transition enabled KONE to significantly reduce callouts by over 40%, proactively identify more than 70% of faults, and achieve a near 100% provisioning success rate. As a result, KONE improved operational efficiency of its smart elevators and escalators, reduced costs, and enhanced customer satisfaction through more reliable and smarter urban mobility solutions. Full story: KONE Unlocks New Efficiencies Using AWS IoT
2. Frontmatec, a leading machine manufacturing company in the meat industry, faced challenges in integrating diverse data streams and ensuring data contextualization for predictive maintenance and global performance management of their machine solutions. Frontmatec leveraged AWS IoT SiteWise Edge on Siemens Industrial Edge to accelerate development of its own customer service portal with offerings for global machine performance management and predictive maintenance. This solution reduced deployment time from several hours to 15 minutes, enabling efficient machine health monitoring and real-time operational adjustments. As a result, Frontmatec enhanced their service offerings, providing smarter, more efficient automation solutions to their customers. Full story: The power of edge-to-cloud integration in manufacturing: How Frontmatec accelerates time-to-value of machine digital services with Siemens and AWS
3. Castrol, a subsidiary of BP that provides lubricants and services for marine, industrial, and automotive industries. Castrol faced the challenge of improving and automating its used oil analysis (UOA) process, which was traditionally time-consuming and manual, leading to delays in maintenance and outdated metrics. The solution was to develop Castrol SmartMonitor using AWS IoT services such as AWS IoT SiteWise and AWS IoT Core, enabling near-real-time monitoring and analysis of oil quality. This implementation reduced operational downtime, waste and maintenance costs while enhancing data accuracy and near-real-time monitoring compared with waiting up to 3–8 weeks. As a result, customers experienced significant cost savings, including $100,000 in repair costs during a trial, and improved operational efficiency with early issue detection and proactive maintenance. Full story: Automating Lubricant Analysis with Castrol SmartMonitor Using AWS IoT SiteWise
4. Schenck Process Group, a global market leader in B2B measurement and process technology, faced the challenge of integrating and measuring diverse and vast range of data points from many different sensors to offer predictive and data-driven maintenance to their clients. These sensors are positioned on machines across the globe, often in remote locations. The solution, implemented by Storm Reply, an AWS Premier Tier Consulting Partner, using AWS IoT services, involved creating a scalable and reliable IoT platform with AWS IoT Greengrass for edge processing and AWS IoT Core for secure device management and data ingestion. As a result, Schenck Process achieved enhanced machine monitoring and predictive maintenance capabilities for their B2B customers, leading to improved service offerings and operational efficiencies. Full story: How Storm Reply Enables Industrial IoT and Predictive Maintenance at Schenck Process Group with AWS IoT

Conclusion

In conclusion, leveraging AWS IoT and AI managed services offers manufacturers a transformative approach to building smarter, more efficient, and secure industrial products. By addressing common challenges such as edge processing, data integration, security, and operational efficiency, these services enable manufacturers to focus on core innovations and enhance customer value. Real-world applications, like those from KONE, Frontmatec, Castrol, and Schenck Process, demonstrate significant improvements in remote monitoring, predictive maintenance, and overall operational performance which can enable new business models and revenue streams. Embracing these technologies positions manufacturers to stay competitive and drive future growth in the their markets.

Ready to transform your industrial operations? Explore the power of AWS IoT and AI managed services to build smarter, more efficient, data driven and secure industrial products. Whether you’re looking to enhance machine monitoring, implement predictive maintenance, or streamline data processing, AWS has the solutions to meet your needs. Start your journey today and see how industry leaders have achieved remarkable results. Visit the AWS IoT Portfolio home page to learn more and get started. https://aws.amazon.com/iot/