Fault diagnosis of rolling bearings based on improved FMD and Autogram

Autogram enables precise extraction of both demodulation band and fault characteristics. However, in presence of strong noise and complex interference, fault features extracted by Autogram may become indistinct, contributing to inaccurate identification of faults. This work proposes a fault detection approach for rolling bearings, integrating an enhanced Feature Mode Decomposition (FMD) algorithm with Autogram technique. First, The parameters of the FMD algorithm are finetuned through the Sparrow Search Algorithm. Following optimization, the FMD is utilized to break down the original vibration signal into several modal components. Subsequently, permutation entropy is calculated for each component to identify optimal one. Finally, Autogram is used to diagnose faults based on selected optimal component. Data from an experimental platform demonstrates that, compared to Fast Kurtogram and Autogram, proposed method significantly reduces noise and random interference. This improvement facilitates precise extraction of fault characteristic frequencies in rolling bearings.