Vives Javier, Palací Juan
Department of Systems Engineering and Automation, University Polytechnic of Valencia, 46022 Valencia, Spain.
Red Engineering Technology Limited, Wolverton, Milton Keynes MK12 5DJ, UK.
Sensors (Basel). 2022 Oct 9;22(19):7649. doi: 10.3390/s22197649.
In this work, we combine some of the most relevant artificial intelligence (AI) techniques with a range-resolved interferometry (RRI) instrument applied to the maintenance of a wind turbine. This method of automatic and autonomous learning can identify, monitor, and detect the electrical and mechanical components of wind turbines to predict, detect, and anticipate their degeneration. A scanner laser is used to detect vibrations in two different failure states. Following each working cycle, RRI in-process measurements agree with in-process hand measurements of on-machine micrometers, as well as laser scanning in-process measurements. As a result, the proposed method should be very useful for supervising and diagnosing wind turbine faults in harsh environments. In addition, it will be able to perform in-process measurements at low costs.
