Fault diagnosis of rolling bearings grounded in SBOA optimisation of VMD parameters and Transformer-BiGRU

Addressing the significant challenge of extracting fault features from rolling bearing vibration signals amid complex working conditions and noise interference, a fault diagnosis approach grounded in the combination of the secretary bird optimisation algorithm (SBOA), optimised variational mode decomposition (VMD) and the Transformer-bidirectional gated recurrent unit (Transformer-BiGRU) model is put forth. The Transformer-BiGRU model is a skilfully integrated fault diagnosis method that is predominantly intended to heighten the accuracy. The SBOA first optimises the VMD parameters to decompose the vibration signal into multiple modal components. To be specific, each modal component is adopted as an input for feature extraction and abstraction by Transformer. The features extracted by Transformer are subsequently subjected to time-series modelling and feature fusion by the bidirectional gated recurrent unit (BiGRU). Ultimately, the fused features are employed for the diagnosis and classification of bearing faults. As suggested by the research findings, the approach can achieve an average accuracy of 98.80% in the presence of noise interference. It can even reach 100% when the signal-to-noise ratio (SNR) is 6 dB and the model can also achieve a fault diagnosis rate of 97.90% when the load is changed. Hence, the fault diagnosis approach proposed features high accuracy and strong generalisation ability, providing a groundbreaking way of thinking for the fault diagnosis of rolling bearings.