Bivariate empirical mode decomposition and its contribution to wind turbine condition monitoring

Abstract

Accessing difficulties and harsh environments require more advanced condition monitoring techniques to ensure the high availability of offshore wind turbines. Empirical mode decomposition (EMD) has been shown to be a promising technique for meeting this need. However, EMD was developed for one-dimensional signals, unable to carry out an information fusion function which is of importance to reach a reliable condition monitoring conclusion. Therefore, bivariate empirical mode decomposition (BEMD) is investigated in this paper to assess whether it could be a better solution for wind turbine condition monitoring. The effectiveness of the proposed technique in detecting machine incipient fault is compared with EMD and a recently developed wavelet-based ‘energy tracking’ technique. Experiments have shown that the proposed BEMD-based technique is more convenient than EMD for processing shaft vibration signals, and more powerful than EMD and wavelet-based techniques in terms of processing the non-stationary and nonlinear wind turbine condition monitoring signals and detecting incipient mechanical and electrical faults.