A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings

TL;DR

This paper introduces a novel time-frequency Transformer model for fault diagnosis of rolling bearings, leveraging self-attention to improve feature extraction and computational efficiency in vibration signal analysis.

Contribution

It proposes a new Transformer-based framework with specialized tokenizer and encoder modules for fault diagnosis, filling a gap in applying attention mechanisms to this field.

Findings

Outperforms benchmark models in fault diagnosis accuracy

Demonstrates effectiveness on bearing experimental datasets

Validates the superiority of the Transformer approach over traditional methods

Abstract

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.