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This work uses recurrence plots (RPs) to identify nonlinearities and non-stationary conditions in wind turbines. Traditionally, recurrence plots have been applied to vibration or acoustic data; this paper applies them to magnetometer and accelerometer data to compare the sensitivity. The recurrence plots are generated by plotting points in the phase space and identifying those points where the dynamic system returns to a similar configuration, meaning that the state variables are similar to previous conditions. The state variables for the acceleration data are the position and velocity, whereas, for the magnetometer data, they are the magnitude of the magnetic field and its integral. The time series are integrated by combining the shifting principle of harmonic functions and the empirical mode decomposition. The EMD method separates the original signal into several modes, shifts them, and combines them back. The time series were obtained from an accelerometer and a magnetometer mounted in a wind turbine. The results showed that the RP presents different patterns depending on the signal; magnetometer signals identify low-frequency components, such as magnetic field anomalies, and accelerometer signals identify high-frequency components, such as bearings and gears.
