Technical condition control.
The trend of recent years is the transition of modern industries from reactive methods of troubleshooting to predictive maintenance.
This approach has already proven its effectiveness in preventing emergencies and saving on repairs. But what if we consider predictive maintenance not as a cost-cutting strategy, but as a tool for obtaining additional resources?
By equipping the company’s service department with modern means of data monitoring and analysis, you can increase staff productivity. And the released resources are easy to redirect to the implementation of promising ideas.
From 2020 to 2025, the Industry 4.0 tool market will grow at least threefold, to $ 12 billion. To understand why this trend is growing, you need to start with big data. This term refers to large sets of information gathered from various sources. They are so complex and voluminous that they do not fit in any Excel spreadsheet. Systems like SmartEAM read and analyze hundreds of indicators for each unit, look at their dynamics and compare it with the life cycle of the asset. You can imagine how many variables and metrics even a one-day record will contain.
In terms of profits, big data is the ability to find a value needle in a stack of information: a blind spot, a future malfunction, or a space for development. From the point of view of maintenance, the main problem is that these data sets were stored and analyzed independently of each other: in different programs and departments. Now, with artificial intelligence (AI) algorithms, we can analyze and see the full picture of the operation of the equipment on real, accurate and timely indicators.
The big data finds its logical continuation in the Internet of Things (IIoT). This is called a network of “things” (sensors, electronics, routers and controllers), in which devices collect and exchange data. In traditional maintenance there is already a similar approach: pressure, flow or temperature sensors on the units. But here’s the trouble: in enterprises, these sensors often work only within their protocols, ie information can be seen only on the display of the device.
With Industry 4.0, the Internet of Things integrates all sensors into one big-data network. They can be analyzed and get a solid basis for decision-making. Another nice fact: the price of IIoT sensors has been falling steadily since 2004.
Okay, with the collection, structuring and analysis of data on the operation of the equipment sorted out. But we don’t need good graphs and spreadsheets, we need specific business tasks. How to be?
It’s time to get acquainted with the predictive methods (Predictive Maintenance) Industry 4.0, which turn analytics into forecasts. Predictive techniques, with their complex heuristics and algorithms, predict “surprises” in the operation of equipment. For example, if the vibration of the unit’s bearings is higher than the permissible norm, the system will notify you in advance of an imminent accident. Workers will be able to fix the problem at a convenient pace, instead of running headlong into another breakdown, which flies the company into a penny.
We are planning an effective maintenance with Industry 4.0
As you can see, there are enough tools to improve the reliability of equipment and reduce maintenance costs. Today’s technologies are more powerful, clearer and cheaper than any of its predecessors, especially stationary posts for reading.
Implementing Industry 4.0 approaches does not require a complex IT infrastructure, capital investment or a whole staff of experts “on board”. For example, the SmartEAM product is available as a cloud solution – you pay only for the required computing power, not “iron”, wires and space for servers.
You don’t have to implement everything at once: talk to experts, evaluate priority areas, implement modular solutions and wait for quick results. The money you save in the first year (as Interpipe did), you can gradually invest in new solutions.
Therefore, maintenance with Industry 4.0 tools and SmartEAM system is: