Fault diagnosis of offshore wind turbines based on component separable synchroextracting transform

The fault diagnosis of wind turbines is crucial for wind power generation. However, the violent variation in wind power and directions of offshore wind turbines often results in high nonstationary vibration signals, which poses a challenge for effective fault recognition. Time-frequency analysis (TFA) is a common method to reveal the timevarying frequency components caused by faults. In this paper, we propose a component separable synchroextracting transform (CSSET) by exploring the unique modulation characteristics of wind turbine vibration signals. For the method, a fundamental frequency is firstly estimated by a time-frequency ridge optimization method, and the potential fault characteristic frequency (FCF) set is constructed according to the modulation characteristics of the wind turbine vibration signal. Next, the instantaneous amplitudes (IAs) of these components in the set are estimated by a time-varying bandpass filter. Then, the instantaneous frequency (IF) of the reconstructed single component is extracted by the synchronous extraction operator (SEO), and a high-precision time-frequency representation (TFR) is obtained. The proposed method takes full advantage of the physical modulation characteristics of vibration signals and the frequency components which are most relevant to fault information are only preserved. The effectiveness of the proposed method is verified by analyzing simulation and experimental signals.