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Industry 4.0 aims to create an intelligent factory, combining the intelligence of IT systems with automation. In an intelligent factory, the production floor machines are connected to IT systems via Internet or the cloud, and the collected data and automation complements machine learning.
Industry has always been a source of innovation. However, since introducing intelligent factories can pose a risk of interfering with systems that would have otherwise operated efficiently and profitably, there must be valid and demonstrable reasons for such change. The fact that introducing Industry 4.0 relatively quickly can offer real and often significant benefits that come from switching to intelligent factories could be one of the reasons for this change.
Reports agree that industry will account for a large proportion of the things connected in the IoT. Most of these things already exist and there will be a significant transition period where industry looks to retrofit equipment of all ages. As production machinery often represents one of the largest capital investments for businesses, updating production lines is a substantial strategic decision. Changing a piece of equipment just to get an ethernet port isn’t likely to happen, so businesses must look for other ways to connect to the wealth of information trapped in their devices.
Remote I/O devices offer a solution, enabling IoT connectivity and taking a step towards Industry 4.0. Brainboxes develops many devices that can connect to virtually anything, acquire data, and then upload that data to the Internet or the cloud. Understanding how simple data acquisition can get you from machine to information can shed some light on the essence of Industry 4.0 and enable you to discover ways of implementing those new solutions.
Here is the way Brainboxes pictures the journey from machine level to Industry 4 insight and beyond:
All machines generate signals when turned on. A simple example would be a lightbulb that lights up when the machine is powered on. Inside the machine, various signals will be sent: to turn on the fans when the temperature gets too high or to change the light colour when a conveyor belt stops. Outside of the machine, various sensors also generate signals. Many of those signals can be measured using simple meters with two test leads, making machine monitoring easily accessible.
Signals on this level can be very simple (high/low, on/off, 1/0.) and may appear as noise that doesn't mean much on its own. However, such input signals are good enough for remote I/O devices. They can be received from machines, PLCs, HMIs, or independent/additional sensors.
Once you assign values to those signals, you can use them. For instance: if the signal transmitted to the fan is high, that means it is turned on. If you connect it to the conveyor belt's activating system, you will have a ready-to-build machine. Green light indicates that the machine's status is normal. Once the green light turns on and the sensor detects an item, you have a working product.
By saving and analysing a machine's status you can begin to assess how long it had been working during a shift, or how much time it took to complete a full operating cycle and assemble a product. This is the basic information required to calculate the overall equipment effectiveness equation. All gathered information must be stored in databases: on servers, in the cloud, or on various devices. This is known as distributed processing.
Generally, machine statistics can be converted to useful information after some time, based on observed patterns. For example: a machine's operating temperature can be correlated with its damage, which may facilitate optimisation of scheduled maintenance work.
Once you get to this point, you are on the brink of owning a production floor in which mechanical processes are created on the basis of characteristics and nuances that are unique to your business. The information you obtain and changes you implement may be surprising and may extend beyond just the production line.
The final stage of advancing to Industry 4.0 is the introduction of intelligent software that can learn machine patterns and implementing predictive maintenance. This part requires some technical skills. Thankfully, there exist certain platforms designed to help you with it: IBM Watson, Google Cloud Prediction API and TensorFlow, Microsoft Azure, and Mindsphere from Siemens. All of those platforms feature cloud-based solutions enabling data analysis regardless of their volume or purpose.
This software is still at the early stage of its development. Time will tell which platforms become commonly used and which ones disappear. Software from well-known, trusted brands like Siemens can become more widespread in the industry. Open source code software and software designed by IT companies like Google will likely be less prevalent. Companies that have already completed projects designed as a response to industry needs can implement optimised, innovative solutions more quickly and efficiently. The best solutions quickly and seamlessly integrate software into our lives, offering many benefits, driving, facilitating, and integrating numerous functions. Industry 4.0 systems should not be any different.
Brainboxes produces reliable digital and analogue devices that offer excellent hardware and software compatibility. This lets customers take a big step towards personalised Industry 4.0 solutions.
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