Maintenance planning and a reliable enterprise Industry 4.0
When repairing equipment, they do not always fix the problem on time: somewhere they forgot to drain the oil, somewhere they did not change the fuel filter, and somewhere prematurely put the “left” spare part. This is how small grains of sand appear, which create a whole desert of problems that create financial and reputational losses for the company.
Effective MRO is: organization, prioritization and transparency. It sounds simple, but it is almost impossible to develop a single system for hundreds of different units. Here there is a breakdown, there is an accident, there is an overdue replacement of spare parts – go ahead and know if it is possible to structure and control all this at all.
It turns out you can. In this article, we will explain why innovation in equipment maintenance is not only effective, but also not as expensive and difficult as many people think.
Innovation guards enterprise efficiency
From 2020 to 2025, the market for servicing aggregates with Industry 4.0 tools will grow at least three times, 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 collected from various sources. They are so complex and voluminous that they will not fit into any Excel spreadsheet. EAM systems read and analyze hundreds of indicators for each unit, look at their dynamics and compare them with the asset life cycle. You can imagine how many variables and indicators even a one-day record will contain.
An example of how big data works on the line of an enterprise
From the point of view of profit, big data is an opportunity to find a needle of value in a stack of information: a blind spot, a future breakdown, or a space for development. From a maintenance and repair point of view, the main problem is that these datasets 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 complete picture of the equipment operation on real, accurately and on time recorded indicators.
Big data finds its logical continuation in the Internet of Things (IIoT). This is the name for the network of “things” (sensors, electronics, routers and controllers), in which devices collect and exchange data. In traditional MRO, there is already a similar approach: pressure, flow or temperature sensors on the units. But here’s the problem: in enterprises, these sensors often work only within their own protocols, that is, information can be seen only on the device display.
With Industry 4.0, the Internet of Things connects all sensors into one network that generates big data. They can be analyzed and a reliable basis for making decisions can be obtained. Another nice fact: the price of IIoT sensors has been steadily falling since 2004.
Okay, we’ve figured out the collection, structuring and analytics of equipment operation data. But we do not need beautiful graphs and tables, but the solution of specific business problems. How to be?
It’s time to get acquainted with the Predictive Maintenance of Industry 4.0, which turns analytics into predictions. Predictive techniques, with their sophisticated heuristics and algorithms, predict “surprises” in equipment performance. For example, if the vibration of the bearings of the unit 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, and not rush headlong into another breakdown, which costs a pretty penny for the company.
An example of how mathematical models predict equipment breakdowns
The synthesis of EAM systems and predictive techniques allows solving more complex problems. Correctly adjusted business logic will be able to predict breakdowns in units, the condition of oils in units or entire emergency zones. For each situation, the same SmartEAM will be able to select the necessary maintenance methodology and reduce the risk of a sudden shutdown.
In the concept of Industry 4.0, there is another direction that allows machines to better understand the world around them. Machine Vision is a set of algorithms that reads and analyzes data from video cameras. Mathematical models inside machine vision systems recognize and describe an object, and then react to its changes.
When working with equipment, machine vision helps to identify a defect in the line, eliminate a problem with a spare part and collect data even more accurately. Some factories, for example, have cameras that read the vibrations of the equipment. With pixel precision, they process and transmit in numbers at 200 frames per second. The system analyzes, interprets this information and issues recommendations.
We do effective maintenance with Industry 4.0
As you can see, there are enough tools to improve equipment reliability and reduce maintenance costs. Today’s technologies are more powerful, clearer and cheaper than any of their predecessors, especially stationary posts for taking indicators.
Moreover, the implementation of Industry 4.0 approaches does not require complex IT infrastructure, capital investments or a whole staff of experts on board. For example, SmartEAM is available as a cloud solution — you pay only for the computing power you need, not hardware, wires, and server space.
You don’t have to implement everything at once: talk to experts, assess priority areas, implement modular solutions and wait for quick results. The funds that you save in the first year (as Interpipe did) will be able to gradually invest in new solutions.
In total, MRO with Industry 4.0 tools and the SmartEAM system is:
- increased asset reliability
- faster and better setups
- high equipment utilization
- prevention of accidents, downtime and necessary readjustments
- holistic and transparent defect control
- reducing the burden on repairmen due to the IT system
- access to any information from any device from anywhere on earth
Not sure where to start? Contact our experts to find a solution for your business needs.
