Work

Multi scale parallel frequency attention network for bearing fault diagnosis under severe noise

Year: 2026

Type: article

Abstract: Vibration signals collected from harsh environments often suffer from severe noise interference, which can submerge critical fault characteristics. To address this challenge, a frequency attention network based on multi-scale parallel ResNet (MSPResNet-FA) is proposed. The architecture integrates a Fast Fourier Transform Convolution (FFT-Conv) module to extract harmonic frequency impulse feature, while parallel multi-scale kernels in the convolutional layer are employed to extract various scale local frequency features. Furthermore, a Convolutional Block Attention Module (CBAM) is utilized to enable the network to focus on and extract important features from the frequency domain especially the multi-scale harmonic frequency impulse feature. The effectiveness of MSPResNet-FA was validated using four benchmark datasets: Politecnico di Torino (PDT), Xi’an Jiao Tong University (XJTU), Paderborn University (PU), and the Society for Machinery Failure Prevention Technology (MFPT). Experimental results indicate that MSPResNet-FA outperforms current state-of-the-art methods, notably achieving accuracy gains of over 5% on the XJTU, MFPT, and PDT datasets. Ablation studies confirm that the FFT component is foundational to this high performance, contributing an improvement of over 10%, while the CBAM refinement layer provides an additional 1% enhancement.

Source: Discover Mechanical Engineering

DOI: 10.1007/s44245-026-00283-y

Authors: Jungan Chen, Xu Chen, Hang Wang, Ang Lv

Institution: Zhejiang Wanli University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,390