Essential Terms of the Industrial Internet of Things (IIoT) at a Glance
Industry 4.0 stands for the fourth industrial revolution which is on everyone’s lips currently. Together with the Industrial Internet of Things (IIoT), it is based on intelligently connected systems that communicate internally and with its users. This allows a company to manufacture its products as self-organized and effective as possible. Terms such as microservices, digital twin or IIoT platform often fall into this category. We explain precisely what they mean in the following article.
Asset Management is the digital representation of plants and their components. Initially, it does not take place in a networked manner, but rather at a central location. Asset management might be intelligent, but it does not have to be.
Through end-to-end intelligent asset management, users keep track of all their assets (parts, machines, equipment, orders or products) and collect data on these assets. The digital availability of all these data facilitates evaluations, analyses and tests. Thus, possible sources of error are identified early, efficiency remains in focus, and predictive maintenance algorithms are applied. Through asset management, a machine manufacturer, for example, can tell precisely which screw was fitted at which point, where it came from, who ordered it and much more. The company, thus, has a complete inventory of its assets and knows how efficiently they run, how many parts are produced, when the next machine is to be delivered and even how production capacity will be utilized in the future.
Moreover, manufacturers can monitor the digital services of their machines and facilities during operation, and the data collected on all assets allow assessing the equipment efficiency. It shows how efficient a production process is and how the equipment (the facility) is utilized. Higher efficiency means that resources are used rationally generating a high output with the resources used. Overall Equipment Efficiency (OEE) is a well-established indicator for the overall plant efficiency across industries.
One of the essential prerequisites for IIoT applications is data collection. Based on this information, it is possible to calculate, e.g., when the next maintenance is due or how a department can better plan and automate its workload. To monitor these data, visualization is crucial, because, without it, data cannot be evaluated and subsequently processed. For this reason, user interfaces and simple navigation, as known from the B2C sector, are also relevant for B2B.
Nowadays, everyone is accustomed to using smartphones and handy user interfaces in the private sector. So, in business, why should one work with outdated interfaces that slow things down? For B2B software, productivity is paramount. Therefore, data must be made visible and interpretable in a simple way without the need for specialized knowledge. Such visualization can take various forms: from virtual images of the machinery (3D models, simulations) to the simple representation of sensor values, their histories and trends (e.g. temperature plots).
Today, an elevator is much more than just a mechanical device. While the software allows monitoring and controlling of the system, it can simultaneously schedule maintenance work in advance, and potential wearing parts can be re-ordered automatically. Subsequently, data on uptime, cycles or loads may provide valuable information on visitor traffic within the building.
With digital services, customers can be tied much closer to the company. This translates into more revenue, be it from additional license fees or automized spare parts ordering. Besides, something is emerging that has played a rather minor role in the industry so far: Customer experience. Software lets customers get excited about industrial hardware. The interactive visualization of tangible assets on a tablet or mobile phone creates entirely new possibilities for intuitive operation.
A digital representation of a machine or facility is called a digital twin. Its individual tasks are digitally captured, modeled, reproduced or simulated by software. Even a simple representation of a machine based on key performance indicators can be named as a Digital Twin. The individual sensor measurements form the life data of a digital twin. Extracting machine data and the process of creating a digital image improves the overall understanding of plant operation and the machinery. By this knowledge building, products and procedures can be enhanced. The machine data visualization alone can open up new business areas for a company and thus have a positive effect on business results. Furthermore, other IIoT designs can be built on a prototype within a short period.
A microservice is a software architecture that is based on many small and self-contained services representing a sub-function of a larger system. Generally, they do not perform multiple functions at the same time but specialize on a single task[TV1] : For example, a microservice for predictive maintenance can forecast breakdowns based on existing machine data, regardless of interface or technology used. Another microservice might visualize the data, while yet another application reads out data from a machine and writes it to data memory.
Besides, a microservice is always self-contained and can be created using any technology. Only the communication interfaces to other systems need to be defined, which can also be implemented in any programming language. Thus, it is possible to adapt, extend or replace them independently of the entire system. As a result, technology and platform independence is achieved.
In contrast, monolithic software centralizes all functions. Here, everything is developed on a specific platform or with a specific technology which creates dependencies to the platform, programming language, protocols or company.
This is particularly important in the IIoT environment, as only a few standards or platforms have been established so far and organizations need to respond flexibly and autonomously on developments.
A platform is a collection of IIoT services in an application/environment with which a structured and methodical framework is provided in order to implement IIoT applications of all kind. IIoT platforms differ only slightly from that of a classic IoT platform since it is a subdivision or specialization.
Predictive maintenance is an IIoT core component and describes a forward-looking approach, in which machines and systems are pro-actively maintained to keep downtime as low as possible. Timing matters, because not only late maintenance can cause downtime, but too early maintenance also incurs high costs. To find the perfect time, sensors provide the appropriate data. In a simple application, only a limited number of parameters need to be detected (temperature, vibration, weight). For more complex applications, machine learning algorithms (e.g. that can learn by analyzing existing data and tests) are used to detect a bad state at an early stage. This is done by examining and evaluating millions of data sets.
Appropriate actions are then taken on this basis. After all, it is quite possible that a machine which has, according to the manufacturer, a service life of 50,000 hours can run for 100,000 hours or more. Alternatively, the manufacturer recommends replacing a component every 200 hours, which in practice lasts much longer. These simple examples illustrate the cost savings that can be achieved through predictive maintenance.
elunic — Your IIoT Partner on Eye Level
For 15 years, our team has been developing cloud-based software solutions for small and medium-sized enterprises and industrial corporations. We meet the growing requirements of Industry 4.0 and the Internet of Things with more than 30 highly qualified and specialized experts in areas such as Big Data, Predictive Maintenance, IT Security and UX Development.