Time–frequency Transformer with shifted windows for journal bearing-rotor systems fault diagnosis under multiple working conditions

Abstract

This paper proposes a new Time–frequency Transformer with shifted windows method (TFSwin-T) for fault diagnosis of journal sliding bearing-rotor systems under multiple working conditions. Current convolutional neural network models struggle with accurate diagnosis in complex and variable working conditions. TFSwin-T uses time–frequency representations of vibration signals to extract features from typical sliding bearing faults, such as scratch, fretting wear, pitting, and fatigue crack. Our model adapts to multiple speeds and reduces computing power consumption through the use of a Transformer encoder with shifted windows. We demonstrate the effectiveness of our optimal fault diagnosis model structure for both journal bearing and rotor fault diagnosis, with higher diagnostic precision and generalizability compared to benchmark models and other advanced methods in complex working conditions.