Application of Transformer Based on Escape Optimization Algorithm to the Faulty Bearing Classification Problem
Year: 2025
Type: article
Abstract: Deep learning has shown strong feature extraction and classification abilities in fault diagnosis, especially for vibration signal time series. The Western Reserve bearing dataset is a common benchmark for evaluating diagnostic models. This paper proposes a Transformer model optimized with the Escape Algorithm (ESC) to enhance bearing fault feature extraction and classification accuracy. Innovations include a multi-scale convolution-attention fusion module for better local temporal feature extraction and the first use of ESC for Transformer hyperparameter optimization. Experiments on the Western Reserve dataset show the model achieves 96% accuracy on both training and test sets, with improved robustness in distinguishing ambiguous categories. The results demonstrate the model’s effectiveness for complex fault recognition, offering valuable insights for intelligent manufacturing and equipment health monitoring.
DOI: 10.54097/01avec07
Authors: Zhangqi Song, Chenlu Zhao
Institution: Huzhou College
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415140917