A Long-Tail Fault Diagnosis Method Based on a Coupled Time–Frequency Attention Transformer

Bearings are essential rotational components that enable mechanical equipment to operate effectively. In real-world industrial environments, bearings are subjected to high temperatures and loads, making failure prediction and health management critical for ensuring stable equipment operations and safeguarding both personnel and property. To address long-tail defect identification, we propose a coupled time–frequency attention model that accounts for the long-tail distribution and pervasive noise present in production environments. The model efficiently learns amplitude and phase information by first converting the time-domain signal into the frequency domain with the Fast Fourier Transform (FFT) and then processing the data using a real–imaginary attention mechanism. To capture dependencies in long sequences, a multi-head self-attention mechanism is then implemented in the time domain. Furthermore, the model’s ability to fully learn features is enhanced through the linear coupling of time–frequency domain attention, which effectively mitigates noise interference and corrects imbalances in data distribution. The performance of the proposed model is compared with that of advanced models under the conditions of imbalanced label distribution, cross-load, and noise interference, proving its superiority. The model is evaluated using the Case Western Reserve University (CWRU) and laboratory bearing datasets.