19. September 2022
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Machine Learning in maintenance

What company doesn't dream of having systems and machines that maintain themselves independently, so that disruptions to production don't even occur in the first place? Maintenance and machine downtime costs would drop significantly. In many cases, the topic of "machine learning" is still a dream of the future, but the necessary prerequisites for this can already be created today - with the right maintenance software.


The common practice in maintenance

There is no question that digitization has taken off in many areas of life. But it is equally clear that it is far from having arrived everywhere. This also applies to the business environment. Among other things, this fact makes itself felt in the form of obsolete machinery, and not without influence on the common practice in the maintenance of plant and machinery.

Regardless of whether the periodic, condition-oriented or reactive maintenance strategy is preferred in order to maintain plant availability, decisions regarding specific maintenance measures are often made on the basis of personal experience and incomplete information. This is mainly due to the fact that maintenance records and the like often fall victim to illegible and incomplete paperwork. To put it bluntly, in many cases decision-makers are groping in the dark in their search for the most economical maintenance measures.

Against this background, one or the other surely longs for fact-based prediction models in order to obtain information about potential failures at an early stage. Hocus-pocus? Not at all! Because there is indeed a magic word that can make this wish come true. Abracadabra: Predictive Maintenance.

Exploiting the potential of Big Data with the right maintenance software

Today, the desire for predictive models is usually still the father of thought. Because one thing is clear: where there is no digitalization, there is no predictive maintenance. In order to fully exploit the potential of predictive maintenance, it is therefore first and foremost imperative to digitally record and evaluate machine data. The more relevant data is collected, structured and evaluated, the better the predictions about possible malfunctions can be expected. But there is countless machine data. Therefore, suitable tools are needed to tame the data monster called "Big Data" and to make the processed results usable for oneself.

In this way, data-based diagrams and reports on important key figures can be used to uncover optimization potential in order to increase the efficiency and productivity of plants and machines with appropriate measures. In addition, the entire area of maintenance can be documented transparently and completely on the basis of systematic analysis results - and, above all, planned with foresight.

The future of predictive maintenance

The economic benefit of today's predictive maintenance systems is therefore beyond question. They trigger an alarm before a malfunction occurs, which could result in high consequential costs. By predicting potential malfunctions, it is estimated that downtime can be reduced by up to 50 percent and maintenance costs by 20 to 40 percent - an enormous potential. As mentioned earlier, the accuracy of predictions increases with the amount of data processed. A system can make optimal predictions if it continuously adapts to the conditions in the production environment, as these are constantly changing. However, this is only possible if a system independently and automatically interprets the collected measurement data.

Sounds like artificial intelligence and machine learning?

That's right! Data science is generally about generating knowledge from data in order to derive recommendations for action for business decisions - even when it comes to the topic of maintenance.

To this end, Big Data analyses are carried out and supported by algorithms in order to identify patterns, regularities and anomalies on the basis of machine data. The results not only allow realistic condition diagnoses for monitored plants and machines, but also enable realistic and reliable forecasts for predictive maintenance.

Machine learning therefore enables predictive maintenance systems to make independent recommendations for solving problems as soon as the smallest changes occur in the behavior of machines or in the production environment - and this before a malfunction occurs.

Even if the age of the Internet of Things (IoT) and Industry 4.0 has arrived, predictive maintenance based on data science and machine learning is far from being the rule, but rather the exception in the corporate environment. It will certainly be some time before machine learning has conquered the field of maintenance across the board. But even today, with the right maintenance software, comprehensive evaluations can be carried out on the basis of machine data, which provide valuable information for the economic design of maintenance.

Find tools for evaluating machine data in the ADAMOS STORE

Our ADAMOS STORE offers you numerous tools for the analysis and evaluation of machine data as well as malfunctions. This provides you with data-based information and valuable insights into the current condition of your plants and machines and allows you to derive and plan suitable maintenance measures.

Take a look around the digital marketplace for Industry 4.0, get to know our smart software solutions and convince yourself of the benefits.

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