A Physics‐Aware Lightweight Transformer Network for Intelligent Bearing Fault Diagnosis Under Variable Operating Conditions
Year: 2026
Type: article
Abstract: ABSTRACT Existing bearing fault diagnosis methods fail to generalise across variable operating conditions while remaining lightweight for industrial edge deployment. We propose PLT‐Bearing, a physics‐aware transformer that adapts vision transformer tokenisation to vibration signals through overlapping convolutional patch embedding, sampling‐frequency‐guided patch sizing and analytically proved amplitude‐invariant self‐attention. With only 0.52 M parameters and 8.2 ms GPU inference latency, PLT‐bearing achieves 99.2% and 95.8% accuracy on the CWRU and Paderborn University benchmarks, respectively, and 90.3% zero‐shot cross‐load transfer accuracy, outperforming recent lightweight CNNs and Mamba‐based state‐space models.
DOI: 10.1049/aie2.70014
Author: Ali Sayghe
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7164989353