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Capgemini : The Growing Need for Private 5G Networks in Manufacturing Plants

07/29/2022 | 10:02am EDT
The Growing Need for Private 5G Networks in Manufacturing Plants
Vijay Anand - Senior Director (Technology) / Chief IoT Architect, Capgemini Engineering 28 Jul 2022

The IoT world is evolving today with the enablement of various technologies such as data analytics, 5G, edge computing, cybersecurity, robotics, augmented & virtual reality, artificial intelligence, and machine learning. However, there is a huge amount of gap in business performance potential based on the evolution happening in the industrial automation system with respect to technology change as shown in figure 1.

Fig 1: Gaps seen in the manufacturing industry and the trends (Source: Internet (Images))

In the manufacturing industry, there is a growing need for autonomous networks with interconnected sensors and actuators, and for greater collaboration to improve productivity without the need for human intervention. To design such a factory based on autonomous technology, a 5G-enabled private, or dedicated, factory design is the better choice. 5G technology can allow communication between people, devices, and sensors belonging to the same factory, while a private 5G network provides specific services within the factory environment to run critical business operations.

To understand this, let's consider the challenges faced in the manufacturing industry.

Problem Statement 1:

Accidents and injuries are a huge problem in today's manufacturing industry, where thousands of people are injured in factories around the world, costing hundreds of millions of dollars in treatment, rehabilitation, and downtime. The major accidents as shown in figure 2 happen in many factory environments due to factors including: -

  • Poor design of storage tanks for petrochemical products
  • Faulty cooling systems
  • Bad design of air circulation systems
  • Inadequate measures and parameters of factory operations
  • Poor safety awareness
  • Inadequate risk assessment response
  • Poor safety management processes
  • Insufficient knowledge of the chemical properties of petrochemical products especially during storage conditions
  • Inadequate emergency response procedures during machine breakdown
  • Safety protocols not being followed by key authorities during factory operations
Fig 2: Key challenges faced in the manufacturing industry (Source: Internet (Images))

For example: A toxic gas leak in a chemical factory kills people and the workers are exposed to the poisonous gas. This leads to a variety of health issues, such as breathing problems, asthma, gastrointestinal disorders, lung cancer, kidney, liver failure, and eye disorders, all of which could lead to other, additional diseases.

Problem Statement 2:

To address the problem statement 1, many factories require Condition-Based Monitoring (CBM) to measure and monitor the critical operational parameters of various assets and machines to understand their behavior and how it changes in different critical situations. The CBM technique is required to measure various process parameters like pressure, temperature, pH, noise, vibration, and flow and samples such as oil conditions in a wide variety of equipment like pumps, electric motors, combustion engines, gearboxes, fans, electrical control panels, compressed air, and hydraulic systems as shown in figure 3. The measurements taken from this sophisticated equipment, called 'conditional measurements', are essential for regularly identifying the condition of equipment to avoid any major accidents, and require a large amount of data to be managed and processed in real-time systems.

Fig 3: Condition-Based Monitoring (Source: Internet (Images))

CBM is required to enable manufacturing plants to handle both preventive and predictive maintenance and to manage and monitor a large amount of equipment and instruments, all of which generate a high volume of data. It is very important to take measurements at regular intervals in real-time, and to quickly assess data that's generated close to that equipment to manage its critical operations. CBM requires reliable high-end wireless technology, which can interface with different equipment to stream data in real-time. This technology can enable maintenance teams and other factory stakeholders to quickly assess conditions with less latency, generate alerts, and trigger any maintenance activity that needs to be performed immediately to avoid any major damage to a factory's operations. Field technicians, working in a manufacturing plant, should be able to monitor the performance of a range of equipment to reduce the risk of downtime or failure. The collection and analysis of these critical data helps make the diagnosis more accurate for the factory's maintenance and IT team, allowing them to plan for any action required to prevent failure and ensure the continuous operation of the equipment. This, in turn, helps to save money, improve the efficiency of day-to-day operations, and create new revenue streams based on the data being monitored and analyzed. CBM methodology in a factory environment can enable field engineers and maintenance teams to measure the health, reliability, and integrity of the equipment, as well as predicting and preventing major issues before any major failure occurs.

Problem Statement 3:

Across the globe, manufacturing plants have deployed dedicated, or private, networks for managing various items of equipment on their premises, designed and deployed with either Wired Ethernet and/or Wi-Fi technologies. Wired Ethernet-based connectivity is very cheap, as shown in figure 4(a), and provides stable communication quality and performance. Because it is wired, however, it cannot provide mobility: the wiring cost is high, and construction time is too long. Wi-Fi-based instrumentation devices, as shown in figure 4(b), are easy to deploy in the factory, where network deployment cost is low, but there are still drawbacks: wireless communication connections are unstable; the communication distance is short; the latency is longer than tens of ms; it's vulnerable to outside threats, and its mobility is also very limited.

Traditionally, data generated from wired & Wi-Fi-based instrumentation devices installed in manufacturing plants are processed either on the local premises or in the public cloud to control the behavior of these devices. Typically, these devices require highly reliable connectivity for quick communications, a latency of less than 1ms, secure data management and data storage, proper traffic isolation between different critical applications running in the factory, and guaranteed QoS for day-to-day operations managed over the private network.

Fig 4a & 4b: Challenges faced in the factory based on wired (Ethernet) and Wi-Fi ecosystems (Source: Internet)
Methodology and Approach:

Today, digital transformation is happening in various manufacturing plants, focused on business agility, operational efficiency, and the enablement of technologies such as IIoT, cloud, edge computing, robotics, 3D printing, big data, digital twins, AI/ML/DL, and next-generation connectivity like 5G. It is clear, however, that a broad ranging 'one size fits all' approach is NOT sufficient, and each factory must consider what digital transformation could look like for their key business requirements. Service providers (MNOs) traditionally offer wired and wireless connectivity to factory premises via inflexible and static connections due to which certain services gets 'overprovisioned' for peak demand. As a result, factories need to pay more for unused capacity. Manufacturers are therefore looking for service providers as key partners, who can supply 'fit-for-purpose, next-generation connectivity solutions' with the option to request, configure, and modify network resources 'on demand' for various individual requirements and critical applications to meet the demands of expected QoS.

To manage production in a reliable manner, manufacturers must balance the use of conditional maintenance to meet growing demands with various limitations in the traditional Wired LAN or Wi-Fi networks used in the factory environment. These businesses have realized that, to begin their digital transformation and scale up their business value and revenue, using devices like cobots, drones, and AR/VR/MR in manufacturing plants to enable more automation requires a dedicated private network with extremely high throughput, high data speed, strong security, and lower latency and delay to handle the increase in connectivity and data transmission. The growing number of interconnected devices in the factory environment, spanning from sensors to actuators to Programmable Logic Controllers (PLCs), need to communicate with each other seamlessly in real-time for data management, monitoring, and analytics.

To address the needs of factories, and with digital transformation happening in Industry 4.0 today, the establishment of 5G standards and the emergence of private 5G spectrum are opening bigger opportunities for factories to consider private 5G networks. Many large industrial manufacturing plants have their own wired and/or wireless networks, and there are many emerging technologies that demand a significant amount of computation and resources for operations within the factory environment. This has led many manufacturers around the world to seek a private 5G network as a preferred solution for their factory environment. But to realize the full potential of private 5G networks, each factory needs to have its own strategic approach based on its specific application and product requirements.

Fig 5: Private Factory Network based on 5G (Source: Capgemini + Internet (Images)

There are many implementation options for designing a private or dedicated 5G factory network that can be owned and managed either by an MNO or by the factory's IT staff. A 5G network design based on the 3GPP standard, for example, can be built using licensed, license-shared, or unlicensed spectrum. Figure 5 shows the high-level deployment of 5G in a private network based on standalone mode.

There is NO one-size-fits all approach that exists today. Many manufacturing plants have multi-range devices that need to be handled - some industrial devices like AR, VR, and factory surveillance might generate a high volume of data while others, like motors and pumps, generate a lower data rate. As they cover a large volume of equipment, sensors in the factory network will be operating at both high and low bit rates. 5G private network architecture must therefore be able to support low, mid and high band ranges to interconnect 5G NR devices operating at different frequency bands. A 5G private network design has to handle radio frequencies ranging from sub 1 Ghz (low band) to extremely high frequencies (25+ Ghz). The lower the frequency, the farther the signal can travel in the factory environment. The higher the frequency, the more data it can carry over a short distance. Designing a 5G private network architecture for industrial automation systems based on the need to support of all bands, as well as enabling emerging technologies like AR and VR, is not an easy task.

With potentially hundreds of thousands of critical sensors and control systems used in larger factory environments, 5G private network implementations are increasingly finding a way. 5G networks will be powered by massive, distributed computing, located closer to sensors and machines, and capable of applying artificial intelligence and machine/deep learning algorithms to handle huge amounts of industrial and critical data within the factory environment. A 5G factory has a private network design with its own 5G network built in, where 5G devices, RAN, and core are integrated into a complete ecosystem from end-to-end. A private 5G network does not interface with or leverage resources and functionalities from the public 5G MNO network. However, a private 5G frequency is used when a factory creates its own private 5G network, whereas an MNO's publicly licensed frequency can be used if the MNO builds a private 5G network for a factory. MNOs' public 5G networks can be used as backup to an existing private 5G network, enabling it to connect all the manufacturing equipment and devices installed in a factory environment to a public 5G network if the private 5G network fails for any reason.

For example: - Since 5G devices in a private network use the same technology as public 5G MNO networks, a private 5G network can handover the devices to public 5G MNO networks if any of the 5G enabled devices leave their private network's coverage area. To enable a public 5G MNO network as a backup, manufacturers need to have extended connectivity of their private 5G network's operation to a public 5G MNO, so that any 5G devices in a manufacturing plant can use the same SIM in both a private network and a public MNO network. For example, the factory can monitor and control an automated forklift machine after it has crossed the street and moved out of range of its private 5G factory network, where it gets switched over to the public MNO's 5G network based on its inbuilt capability.

A private 5G network solution is a fully integrated ecosystem based on industrial-grade hardware with dedicated 5G radio access and 5G core software modules that provides enhanced data security, broadband speeds, deterministic behavior, real-time response, and cost efficiencies to factories deploying on-premises, business critical applications. As such, it forms part of the backbone for the smart factories of the future, as shown in Figure 6. Private 5G factory network deployments replace wired networks (LAN) and Wi-Fi networks in manufacturing plants, where a fixed communication infrastructure will be more complex due to the increase in the number of CNC (Computer Numerical Control) machines, and industrial devices which require additional wiring. 5G private factory networks are specifically designed to support Industrial IoT (IIoT) applications, where a private 5G network can deliver ultra-low latency and incredibly high bandwidth connections supporting artificial intelligence-driven applications by serving the larger number of sensors and various instrumentation equipment.

Fig 6: Private 5G Network with Network Slicing (Source: Capgemini + Internet (Images))

A private 5G network offers a combination of essential security, reliability, and performance enhancements over other wireless technologies as it has been designed from the ground up to meet business and mission critical application needs in three main categories: Ultra-reliable and Low-latency Communications (uRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communications (mMTC). In addition, network function virtualization (NFV) and software-defined networking (SDN) technologies have also been considered as part of 5G network design for handling data communication. An attractive attribute of 5G networks is network slicing, which allows several applications or services to run on the same physical network by dividing the physical bandwidth among them. Each slice is created during the 5G private factory network design to meet the requirements of various critical applications. In a factory, for instance, one slice might serve video surveillance applications that demand higher speeds, while another would serve robots, for which lower latency is critical. In addition, network slices can be allocated to reduce bottlenecks and improve throughput as workload demands increase.

The distributed architecture within a factory environment based on a 5G network design allows local data processing with machine learning algorithms for handling massive amounts of data and takes care of the security and privacy of the factory network. As a dedicated or private domain of a 5G network, the real-time and non-real-time traffic can be managed at the edge of the factory network by the IT team in a more efficient manner.

Vijay Anand Senior Director, Technology, and Chief IoT Architect, Capgemini Engineering

Vijay plays a strategic leadership role in building connected IoT solutions in many market segments, including consumer and industrial IoT. He has over 25 years of experience and has published 19 research papers, including IEEE award-winning articles. He is currently pursuing a Ph.D. at the Crescent Institute of Science and Technology, India.

Disclaimer

Capgemini SE published this content on 28 July 2022 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 29 July 2022 14:01:36 UTC.


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