Industry 4.0, 5G and AI: delivering a digital future
By Steve Sands, Festo
Manufacturers are under constant pressure to increase production efficiencies, boost profitability and improve product quality, while at the same time enhancing security, safety and sustainability. Industry 4.0 is hailed as enabling industries to achieve all this, and is, without a doubt, gaining traction. It has moved well beyond the concept stage– we are now more than four years into a 20-year digital transformation – although there is still a long way to go until the infrastructure is fully in place and all its benefits can be realised. The next generation 5G wireless networks and use of Artificial Intelligence (AI) will be crucial elements to enable Industry 4.0 to be wholly implemented.
Industry 4.0, connectivity and the cloud
Central to Industry 4.0 is connectivity – both the link between human and machine, and machine-to-machine. Industry 4.0 depends on being able to connect to the application so manufacturers are in a position to use data to gain insight about their assets and make informed decisions to optimise their processes. Industry 4.0 relies on fully networked, adaptive production through intelligent products with “embedded functions” – cyber-physical systems.
The first building block, as shown in figure 1, is the use of smart products on a machine, which gather data about its performance. Smart products already exist with the capability of connecting and networking. For example, Festo’s CPX valve terminals have on-board intelligence and utilise the Industry 4.0 standard OPC-UA communication protocol to enable access to the data generated and stored on-board. Connecting a series of CPX valve terminals to, for example a Festo CPX-IOT Gateway enables manufacturers to gather the required data, aggregating, channelling and filtering it as required. This data then can be accessed for Industry 4.0 services either in the cloud or on-premise, so manufacturers can utilise the data. Cloud hosting services enable data to be viewed securely and globally by manufacturers across the internet, transferred via APIs or exported. On-premise devices are deployed to reduce the amount of data that is transferred to the cloud, saving data transmission and storage costs and further restricting access to confidential information. They are installed in the plant, with manufacturers or service providers storing and utilising the data locally. It is contained within the boundary of the factory. Both have their advantages and disadvantages. Premise services are preferred by some users as more secure, although that is not always the case. However, even if data is not exported off-site it is frequently drawn down from the cloud to provide insight and added value. If data needs to be compared from different production lines and in different countries, then it makes sense to share this information via the cloud.
Once relevant data has been collected it can then be used in a hierarchy of services of increasing complexity and value such as data visualisation and condition monitoring – providing alerts when thresholds are met, to preventative and predictive maintenance. Data can be viewed either on pre-configured dashboards or custom generated dashboards depending on the application requirements. Pre-configured dashboards make it very quick and easy to access information on standard products, whereas generated dashboards use a standard template which is then populated with information relevant only to the applications and viewer’s requirements. Progressing from this first step, Festo has demonstrated how manufacturers can then move forward to using machine learning and AI to gain greater insights from the data.
The ability to get greater value out of connectivity is also demonstrated in Figure 1. When manufacturers store data in the cloud, they have global access, knowing the status of their assets (including configuration, hardware, firmware and software versions), utilisation, how they’ve performed historically and are currently performing. This knowledge enables the manufacturers to operate more effectively: for example to speed up commissioning, increase overall equipment effectiveness (OEE) and save energy. One of the industry use-cases is energy monitoring as it provides a quick, proven payback and ROI. If you know your energy consumption at individual plant, production line and machine level, you can then take relevant steps to control, manage and reduce it.
The 5G network
Excellent connectivity is critical to Industry 4.0 and the rollout of the emerging next generation 5G wireless networks is expected to accelerate Industry 4.0’s adoption. Manufacturers demand speed, secured communications and low latency, and for the first time, industrial automation companies have been involved in the development of the new telephony standard from the outset – ensuring that these characteristics will be provided.
The 5G Alliance for Connected Industries and Automation was established to serve as a central global forum, to help manufacturers apply industrial 5G in the best possible way. Members jointly strive to make sure that the particular interests of the industrial domain are adequately considered in 5G standardisation and regulation. Furthermore, the 3GPP (Third Generation Partnership Project), the industry body tasked with developing global standards for mobile communications, is currently working hard on developing the necessary radio technologies and architectural components that – once finalised – will be able to support Industry 4.0 requirements for vast connectivity, ultra-reliability and ultra-low latency.
One of the main differences between 5G and previous generations of mobile networks lies in 5G’s strong focus on machine-type communication and IoT. 5G supports three essential types of communication which are all key requirements from smart factories. These are: ultra-reliable low-latency communications (URLLC), massive machine-type communication (mMTC) and enhanced mobile broadband (eMBB).
Security concerns are also being addressed with 5G. In a recent survey conducted by Festo 67% of respondents voted security as one of the main barriers to Industry 4.0 adoption, as shown in table 1, and it is frequently cited as a key challenge to overcome in implementing Industry 4.0.
Table 1. Main barriers to Industry 4.0 adoption
5G can be characterised as a modular communication system, with in-built privacy and security, which is based upon the cloud approach and can be flexibly configured to meet different service requirements. 5G includes strong E2E security. In particular, mutual authentication between the device and the network is supported. All transmitted data is encrypted E2E between the device and the network. 5G also supports a flexible authentication framework with the Extensible Authentication Protocol (EAP) and strong encryption, while meeting strict latency requirements.
While the standards are not expected to be ready until 2020 at the earliest, in the UK, the Worcestershire 5G consortium project is already exploring ways to increase productivity using robotics, big data analytics and augmented reality with 5G in a manufacturing setting. Promising application areas range from logistics – for supply and inventory management, through robot and motion control applications, to operations control and the localisation of devices and items. 5G is likely to support various Industrial Ethernet and Time Sensitive Networking (TSN) features, thereby enabling it to be integrated easily into the existing (wired) infrastructure, and in-turn enabling applications to exploit the full potential of 5G with ease.
AI is taking Industry 4.0 further
Like 5G, AI is also expected to accelerate and enhance the implementation of Industry 4.0, and connectivity is vital for AI to succeed. But what is AI in the context of industrial automation? AI can be defined as the concept of improvement and gaining insights through smart analytics and modelling, it is a collective term that incorporates a number of steps as shown in figure
AI can take place in all three locations shown in figure 3: in the cloud, where large quantities of data can be evaluated easily, on-premise, which is on the system at production network level, or on-edge, which is on the component at field level.
Machinery will be increasingly autonomous and is expected to use AI to organise cooperation among themselves, sharing data with the supply / delivery chains and with users: creating ad hoc networks as the need arises. The data produced from the manufacturing process is analysed and actioned through AI to create dynamic self-learning production environments that are able to provide increasingly higher levels of productivity, operating with higher quality in a safer working environment.
Some are concerned that AI will dramatically decrease or even eliminate the need for human interaction on the factory floor. However, AI needs human involvement, to define the objective and refine the output. AI does not provide a definitive ‘yes / no’, but rather gives a suggestion with a probability score against it. AI gradually improves the accuracy of the probability score based on the human feedback, refining its algorithm model.
Manufacturing production and the digital world are merging, making factory automation more flexible, increasing energy efficiency, linking logistics processes more closely and optimising the value chain. 5G networks and the emergence of AI are key enablers for the digital future and will offer manufacturers the chance to build smart, digital factories using Industry 4.0 principles.