Work

A Physics-Guided Raw-Dominant Gated Fusion Method for Fine-Grained Bearing Fault Diagnosis

Year: 2026

Type: article

Abstract: Fine-grained bearing condition diagnosis is challenging because different bearing states within the same fault location often exhibit similar fault-characteristic-frequency responses, making them difficult to distinguish using envelope-spectrum information alone. To address this problem, a physics-guided raw-dominant gated fusion network (PG-RDGFN) is proposed for fine-grained bearing fault diagnosis. In the proposed framework, the raw vibration signal is retained as the dominant information source, while the envelope spectrum provides complementary fault-modulation evidence. A physics-aware descriptor derived from bearing characteristic-frequency responses is incorporated into a sample-wise gating mechanism to adaptively regulate the contribution of the envelope-spectrum features. Distinct from conventional direct multi-branch fusion or physics-informed schemes that mainly use physical knowledge as an auxiliary input or regularization constraint, PG-RDGFN uses mechanism-derived physical confidence to regulate how much auxiliary envelope-spectrum evidence participates in the fusion, rather than directly using the physical prior as a fine-grained classification cue. Meanwhile, a physical-consistency loss constrains the learned gate using a scalar physical-confidence target, and a raw-branch auxiliary loss preserves the discriminative capability of the dominant raw representation. Experiments are conducted on an eight-class diagnosis task constructed from the Paderborn University bearing dataset. Compared with SVM, MLP, 1D-CNN, CNN-LSTM, and TCN, the PG-RDGFN achieves the highest accuracy of 98.87%. Ablation and gate-consistency analyses further verify the effectiveness of the envelope branch, gated fusion, physics guidance, and auxiliary supervision. These results demonstrate that PG-RDGFN provides an accurate and physically interpretable solution for fine-grained bearing condition diagnosis.

Source: Applied Sciences

DOI: 10.3390/app16168337

Authors: Chuanbo Wu, Guoao Jiao, Yongdi Zhang, Kangning Jin, Zihang Zhang, Zeming Li

Institution: Hebei University of Science and Technology

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: Hebei Provincial Department of Bureau of Science and Technology