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Stephan Schnabel, PhD in Machine Elements at Luleå University of Technology. Photo: Ted Karlsson.
Stephan Schnabel, PhD in Machine Elements at Luleå University of Technology. Photo: Ted Karlsson. View original picture , opens in new tab/window

Better interpretation of signals save money

Published: 1 November 2016

By interpreting the acoustic waves that ball bearings and gearboxes generate companies will be able to detect problems in time. Such improved monitoring allows machine components to be used much longer. This is presented in a new dissertation at Luleå University of Technology.

The thesis has a basic research approach in which Stephan Schnabel made experiments to verify the sources and to identify the contact properties that affect the acoustic waves.

– My research may improve the processing of signals from acoustic waves, which means that the condition monitoring of machine elements will be better. This is something that is only used on larger machines, in for example, LKAB, paper mills, wind turbines and ore trains, says Stephan Schnabel.

Stephan Schnabel is the first doctorate within the SKF–LTU University Technology Centre, a collaboration between the University and the multinational industrial company SKF. At the centre, a number of PhD-students conduct research on advanced condition monitoring where the goal is to develop the smart bearings of the future.

Making signal processing more reliable

In today’s business, machine elements are changed prematurely, choosing the safe approach. Stephan Schnabel believes his research may help other researchers in signal processing so that it becomes more reliable and more detailed. It will save money and resources because fewer bearings are needed.

– We all know a bicycle chain and know that when it is not sufficiently lubricated you can hear it creak. For gearboxes, bearings and other machine elements it is the same. But the frequencies used to detect errors in these are much higher than for a bike chain. My research raises awareness of these waves and can further improve the condition monitoring of large machines that I mentioned earlier. At high speeds new signal processing techniques to detect errors are required. My colleague Sergio Martin Del Campo at SKF-LTU UTC for example makes progress in this area and it has a connection to my research.

Focus on understanding the problem

– What has been the most fun with this research is that it was very basic. I could really focus and immerse myself in the problem itself. Sometimes when you do applied research, the solution rather than the problem is in focus. More research in this area is needed, however, and I have worked closely with my colleagues who are trying to take advantage of my results. My research can also help to evaluate the interpretation of the signals of existing condition monitoring systems, says Stephan Schnabel.

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Photo: Ted Karlsson

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