Enabling Industry 4.0 and smart factories with IoT and AI


Co-authored by Kumar Shiv Subramanyam

The confluence of smart sensors and Artificial Intelligence (AI) is set to redefine the industrial world. Experts believe a wave of new technologies is creating the fourth industrial revolution or Industry 4.0. Defined by the trend of large industrial enterprises adopting automation, machine learning (ML) and real-time insights and configuration, Industry 4.0 will make global industrial operations smart and efficient.

Industry 4.0 comprises of four key pillars:

  • Machine-to-machine (M2M) communication connected to the plant’s network using Internet of Things (IoT) standards-based automation.
  • Decentralized decision making by the cyber-physical system in an autonomous manner.
  • A mechanism to predict failures and issue alerts in real-time for optimal Overall Equipment Efficiency (OEE).
  • Creating enriched digital twins populated with sensor data from the plant or field.

Industry 4.0 will lead to the creation of Smart Factories with machines that can communicate with each other. Backed by AI and a wealth of insights from raw data, these machines could be empowered to configure production processes and make modifications in real-time to optimize operations. However, several hurdles are delaying the onset of Industry 4.0.

The hurdles of legacy equipment

Building and deploying a plant or factory is a capital-intensive, long-term commitment. Factories and production lines are created on a timeline of decades. Industrial production hubs are slow to adapt and are often burdened with legacy infrastructure that isn’t readily compatible with the latest technology.

Contemporary plants and factories have machines that are predominantly analog. Even the digital machinery in most factories is backed up with Programmable Logic Controllers (PLCs) that are outdated and incompatible with newer sensors and IoT platforms. Machines with analog gauges and outdated PLCs provide critical data that must be processed manually, which can magnify errors in the production process and have a negative impact on OEE.

Machines connected to Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) are more advanced than these legacy machines, but they don’t enable a comprehensive and granular management system that can make an industrial plant truly ‘smart’.

By studying these hurdles closely and creating computing elements and equipment that can solve these challenges, we believe we can kickstart the Industry 4.0 revolution and encourage the mainstream adoption of IoT technologies.

Making legacy factories ‘Smart’

At Microsoft, we’re focused on creating technologies that can serve as the connective tissue between machines. This can help legacy hardware and factories become ‘smart’. Our efforts are aimed at creating a platform that enables real-time data ingestion from factory machines (old and new), local data processing with low latency and comprehensive visualization for smarter factory monitoring. These elements are at the core of Industrial IoT (IIoT).

Azure IoT Edge enables real-time data ingestion and processing. It can adapt to the various open-source and industry standard protocols that different manufacturers and vendors have adopted to make their facilities ‘smart’. It augments industry and web protocols such HTTPS, AMQP and MQTT with added security features and ML. This means the sensors, equipment and vendor software these factories deploy can remain cost-effective and have a small footprint, while Azure IoT Edge powers them to collect data securely through an encrypted channel and process it in real-time with advanced ML algorithms.

Azure IoT Edge also offers cloud-connectivity and full support for modern Open Platform Communications Unified Architecture (OPC UA). Azure IoT Edge modules can be easily built and deployed to convert legacy hardware into smart technology.

Azure IoT Hub acts as the backend layer of IIoT. It includes telemetry, management and operational endpoints. It also enables device provisioning, device twins and routing. This layer enables users to effectively process and visualize data received from all the machines.

Typical Architecture using Azure IoT

Azure IoT Hub integrates with KEPServerEX, a popular connectivity tool, to allow users to operate and configure different automated devices such as RTU, OPC servers and PLC from a single platform. This simplifies the process of connecting machines together.

On both Azure IoT Edge and Azure IoT Hub, ‘device twins’ create a digital replica of physical machinery. These virtual machines store information about the machine’s physical features, configuration, eTags, reported properties and observed properties in the form of a JSON file on both layers.

Complying with industry standards for seamless interoperability

Interoperability and compatibility are crucial as billions of new internet-enabled devices are created across the world and fitted to machines and equipment manufactured by different companies. We have worked with the OPC Foundation to ensure our IIoT platforms comply with OPC UA frameworks and are interoperable with machines that based on these industry-standard protocols.

Our connected Factory Suite is also compliant with these standards. The suite includes software such as Connected Factory, Remote monitoring, Predictive Maintenance and Device simulation, all of which can ingest data, publish it on platforms and save data on the cloud through OPC-UA clients.

For devices that do not use OPC-UA protocols, we use KEPServerEX to connect to the cloud and stitch the data together to create visualizations using Azure Platform Services.

Augmenting IoT with Machine Learning

In our view, ML and IoT are symbiotic technologies. With custom modules and a few modifications, Azure IoT Edge can be infused with more capabilities and features. This ‘Intelligent Edge’ applies ML and data analytics to further empower IoT devices. Modules can deploy an Azure Machine Learning model so that machines can learn from the data they collect and improve their operations over time. Another module can enable Azure IoT Edge to deploy Azure Stream Analytics to process telemetry data in real-time with windowing functions and SQL statements.

Adding modules to Azure IoT Edge can help each machine in the factory run in a separate container, learn from the data it generates, continue operating even when internet connectivity is lost and respond instantaneously to critical problems and issues in the system. These modules can be custom built and modified using simple SDKs available on Azure. Users can access these modules and configure them through any operating system and some of the most common programming languages.

Making IoT mainstream

The next industrial revolution will be built on a layer of smart and internet-enabled devices across the world. While IoT is set to have a tangible impact across industries, legacy machinery, lack of connectivity and a fragmented market have hindered its widespread adoption.

At Microsoft, we believe that pushing this evolution forward is a core part of our mission to empower every person and every organization to achieve more. With Azure IoT Edge and Azure IoT Hub, businesses can connect their legacy hardware to cutting-edge IoT technologies. The two layers create a web of industrial machines that collect data, learn from it, and take real-time actions to resolve issues. We are working on helping these machines connect via industry standard protocols, store data on the cloud and analyze data through predictive tools and ML.

The oncoming wave of ML and IoT technologies promises to transform the global industrial complex. Although the devices required for this evolution are ready to deploy, legacy systems and old equipment in factories are holding the progress back. We’re working to create the connective tissue between the old hardware in factories, new sensors and monitoring desks around the world. We believe a holistic platform that can collect and process data instantaneously, improve processes through ML, and visualize results clearly for users will open the doorways to the next industrial revolution.

Shailendra Miglani is part of Global Black Belt Team at Microsoft Corporation and is responsible for the adoption and deployment of Microsoft’s IoT Strategy and Azure IoT solution offerings across India.

Kumar Shiv Subramanyam is a Technical Architect in the Microsoft Technology Center (MTC) at Microsoft Corporation and provides architectural and technical design solutions on Microsoft Azure Cloud specifically on IoT, Apps, Cloud Native applications and Blockchain.


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