  The scope of data-driven fault diagnosis models is greatly extended through
deep learning (DL). However, the classical convolution and recurrent structure
have their defects in computational efficiency and feature representation,
while the latest Transformer architecture based on attention mechanism has not
yet been applied in this field. To solve these problems, we propose a novel
time-frequency Transformer (TFT) model inspired by the massive success of
vanilla Transformer in sequence processing. Specially, we design a fresh
tokenizer and encoder module to extract effective abstractions from the
time-frequency representation (TFR) of vibration signals. On this basis, a new
end-to-end fault diagnosis framework based on time-frequency Transformer is
presented in this paper. Through the case studies on bearing experimental
datasets, we construct the optimal Transformer structure and verify its fault
diagnosis performance. The superiority of the proposed method is demonstrated
in comparison with the benchmark models and other state-of-the-art methods.
