October 2019

| 30 | October 2019 www.smartmachinesandfactories.com | APPLICATIONS | 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). URLLC facilitates highly critical Table 1. Main barriers to Industry 4.0 adoption Fig 2: The progression of AI

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