The ‘Internet of Things’ sounds like the title of an intriguing film. That was the case until a few years ago when all systems were connected to each other via the internet and a whole new world of applications burgeoned.
Data were suddenly available to be consulted everywhere you looked. But the applications were not yet ready and were mainly limited to the exchange of data, which was not taken up on a large scale. Even the applications were not always useful. I remember the introduction to a smart toaster, connected to the internet, which could burn the weather forecast into a piece of toast.
The development of software solutions has ensured that data can be used more than ever before. The results can be fed back into the systems, known as ‘self-learning’ or ‘machine learning’.
Data have always been relevant in a production environment. However, while data were once intended to be used instantaneously or for analysis of machine availability, without much additional information, actions or decisions, the Industrial IoT (IIoT) has now been introduced to the production environment, with the digitisation and implementation of IT in Operation Technology.
Origins of the Internet of Things
The Internet of Things (IoT) is defined as a network of physical objects connected over the internet, whereby data can be sent, received and exchanged. The term was used for the first time in 1999 by Kevin Ashton, a British pioneer of technology. Definitions of the IoT vary widely, as it is a concept more than a specific technology . IoT has a broad scope and can therefore be used in a wide range of applications. The technical aspects of IoT encompass the capture, transport, storage and analysis of data.
The IoT provides many opportunities for companies and organisations, to increase user engagement, to optimise the use of technology and all types of machines and devices, to reduce waste or to simplify the capture of critical data. In the future, the IoT will increase operational efficiency and product and customer experience and will assist in the development of new disruptive business models.
The IoT is now mainly used in consumer products but the potential for industrial applications is considered even greater, both for industrial operational processes and for the products used in those processes.
The industrial IoT value chain and technical aspects of the industrial IoT
The industrial IoT value chain has been described by experts in various relevant fields. The first step is data capture, using sensors developed according to the signals to be detected. The systems that drive machines and production lines also contain a great deal of valuable data. The data are used locally by operators, although by then, those data can already be outdated or no longer relevant to the operators. The data can, however, include content and additional information (also known as ‘metadata’) and central capture of these data can constitute an important source of information. Of course, both hardware and software are required to do so.
The second major step in the value chain is data transfer. The data can be quite simple, e.g. temperatures or vibrations, or can be complex data, used for calculations. The latter is often referred to in terms of ‘smart sensors’ or ‘edge computing’, because the calculations are not carried out in the cloud but on ‘the edge’ of the network. This is made possible by IO-Link sensors (see also www.IO-Link.be). The data transfer can take place via wired or wireless industrial networks (see Industrial Networks).
The data is then stored at datacentres, on platforms often referred to as ‘the cloud’. These datacentres are either owned by major players such as Amazon, Alibaba or Google, or are proprietary to a company. Data ownership and access will play a significant role in the near future. Europe wants to develop its own database.
Data capture is not an aim in itself, rather the data can be used in many ways. This brings us to the applications to make use of this data e.g. Artificial Intelligence (AI), Condition Monitoring (CM), Data Analytics (DA) and Cloud Computing (CC). Cloud Computing is the processing and conversion of data into other forms that can be useful for specific devices. However, Cloud Computing is increasingly being replaced by edge computing, with sensors and edge systems becoming smarter and computer systems becoming more and more compact and their processing power increasing exponentially, so the technology can be built into edge equipment.
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Industrial IoT for products and production environments
IoT applications can be roughly classified, on the one hand, into product environment level applications, i.e. connected machines and connected production units. On the other hand, products are made smarter, to take advantage of new business models for these products.
The IoT in manufacturing
One of the most important applications in the production environments is mapping equipment availability and usage. The availability and operational data of a machine can be captured via the network. The number of hours a machine is operating (compared to its downtime) can therefore be measured and monitored. However, not all production unit equipment is now connected, in particular if it is older. Additional hardware and software can be used to connect the equipment, to enable IoT solutions. Machines are also fitted with communications modules to capture the data. New machines are Industry 4.0-ready and the basic communications systems are built in.
Ensuring these applications are upscaled to the entire production environment requires the cooperation of the value chain as a whole: from manufacturing companies to machine manufacturers, integrators and digital solution developers. The shared challenge for these companies is to keep up to date on the rapidly developing opportunities. Agoria and Sirris follow these developments closely (e.g. the development of scalable connectivity solutions based on open source standards ).
The IoT and smart products
The application of IoT can add value to products in many ways. The most obvious advantage is that products can be monitored and controlled remotely. The exchange of data and interaction with other products and platforms mean product systems and solutions can be created on a larger scale. The options reach further than adding smart functionalities. A wide range of digital support services can be created by linking the product to software applications outside the product,@@JOIN@@@ e.g. predictive maintenance and custom functionalities. Even the complete operations of a product could be offered as a service. All this can only happen if the product usage data is centrally captured and processed.
The new opportunities of IoT products go hand in hand with new challenges for the companies developing the solutions of tomorrow. The technology may not be the only new element for a company; steps may also be required at business or organisation level. Agoria and Sirris therefore published the WAT SLIM Guide (‘How Smart Guide’) after completing the project known by the same name. This guide maps the most significant challenges for smart connected products.
Condition Monitoring (CM) – one of the applications found in manufacturing companies –consists of monitoring the condition of specific equipment or part of the production system. Installing sensors on a motor, e.g. to measure vibrations (frequency, vibration unit size, etc.) and other values, means the temperature and other parameters can be monitored to detect issues or deviations. The company can therefore plan preventive interventions or maintenance work before a defect occurs or the motor seizes up or breaks down. This principle can also be implemented throughout the machine or production unit.
Making products smarter is not the objective as such, rather it is a step towards the development of a new business model or to add value to a product. Fitting sensors to a product means other applications can be developed and brand-new services be linked to the product. The additional data can be sent to the product manufacturer and used in a new business model, e.g. to guarantee the availability of enough raw materials to continue using the product. A specific example is the fitting of sensors to printers or print cartridges, to automatically recommend the user order new cartridges when the current ones are nearly empty, or even ordering them automatically under a specific contract.