Work

KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis

Year: 2025

Type: article

Abstract: Fault diagnosis methods based on deep learning have been widely applied to bearing fault diagnosis. However, current methods usually diagnose on the same individual device, which cannot guarantee reliability in real industrial scenarios, especially for new individual devices. This article explores a practical cross-individual scenario and proposes a Kolmogorov–Arnold enhanced convolutional transformer (KACFormer) model to improve both general feature representation and cross-individual capabilities. Specifically, the Kolmogorov–Arnold representation theorem is embedded into convolution and multi-head attention mechanisms to develop novel Kolmogorov–Arnold enhanced convolution (KAConv) and Kolmogorov–Arnold enhanced attention (KAA). The adaptive activation function enhances its nonlinear modeling ability. Comprehensive experiments are performed on two public datasets, demonstrating the superior generalization of the proposed KACFormer model with a higher accuracy of 95.73% and 91.58% compared to existing advanced models.

Source: Applied Sciences

DOI: 10.3390/app15147932

Authors: Shimin Shu, Muchen Xu, Peifeng Liu, Peize Yang, Tianyi Wu, Jie Yang

Institution: China University of Mining and Technology - Beijing

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China