Fault diagnosis method for wind turbine rolling bearings based on Hankel tensor decomposition

In order to diagnose the wind turbine rolling bearing faults with vibration signals effectively, a fault diagnosis method based on Hankel tensor decomposition is proposed. Firstly, IMF-SVD (intrinsic mode function, IMF; singular value decomposition, SVD) is used to estimate the number of sources in sensor observation signals. Secondary, a third-order Hankel tensor is formed by the observation matrix, and a set of low-rank tensor subterms are obtained by tensor rank-$\lpar L_r{\rm \comma \; }L_r{\rm \comma \; 1\rpar }$(Lr,Lr,1) decomposition. The fault features of each source are contained in the first and second modes of the corresponding subterm. Then, the source signals are reconstructed by the subterms. Finally, the envelope spectra of the reconstructed source signals are analysed, and the fault characteristic frequencies are extracted. The results of simulation and practical case analysis show that this method can realise the fault diagnosis of wind turbine rolling bearings correctly and effectively.