Aug 2026· Measurement science and technology· Vol 37, pp. 356103· 0 citations· 26 references
Physics
TL;DR
Experiments demonstrate that the proposed denoising diagnostic framework, termed the dictionary-wavelet attention network, provides stronger noise robustness than mainstream deep-learning diagnostic models.
Abstract
Reliable operation of bearings in complex industrial environments is essential. To mitigate the adverse effect of noise interference on fault-pattern recognition in bearing fault diagnosis, this paper proposes a new denoising diagnostic framework, termed the dictionary-wavelet attention network. The framework integrates a dictionary-based denoising autoencoder (DDAE) and a wavelet time–frequency attention (WTFA) module. In the DDAE, the decoder matrix is parameterized as learnable convolutional atoms with different structural preferences, and structured reconstruction is achieved through atom responses driven by encoder-predicted coefficient maps. The WTFA module highlights the principal fault-related informative bands and their neighboring regions, thereby enhancing the specificity of feature representation. Experiments on the Case Western Reserve University dataset and the University of Ottawa variable-speed bearing dataset show that, under severe Gaussian white noise contamination (SNR = −4 dB), the proposed method achieves diagnostic accuracies of 95.94 ± 0.18% and 90.06 ± 0.25%, respectively. Under severe pink noise contamination (SNR = −4 dB), the corresponding accuracies are 92.95 ± 0.17% and 77.03 ± 0.57%, respectively. These results demonstrate that the proposed method provides stronger noise robustness than mainstream deep-learning diagnostic models.
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha· International Journal of Dyn...· 0 citations
Strong broadband noise can mask the weak impact responses produced by bearing faults, while wavelet reconstructions selected only by signal-domain scores may not provide useful inputs for diagnosis. To address this problem, this paper presents WPMSR-1D-ResNet, a training-aware wavelet-domain controlled-noise augmentation framework. PKPM-guided particle swarm optimization first generates scale-specific perturbation candidates in the DWT detail coefficients. Signal-fidelity constraints remove distorted reconstructions, and a proxy CNN with identity fallback selects a global candidate according to validation Macro-F1. The final 1D-ResNet is trained jointly with the measured waveform and the selected augmented view, whereas inference uses only the raw signal. Under one fixed data construction and a common fixed 20-epoch budget, WPMSR-1D-ResNet achieved 0.8311±0.0607 Macro-F1 at the predefined CWRU low-SNR endpoint and ranked third among eleven methods, placing it within the leading statistical group. Its paired mean exceeded raw-signal 1D-ResNet and per-slice PKPM replacement by 0.05582 and 0.05227, respectively, with gains in nine of ten paired computational seeds. Mixed-SNR training increased mean Macro-F1 across eight mismatched-noise conditions from 0.5463±0.1320 to 0.6105±0.1292. The method ranked second on PU and third on acquisition-held-out AT data; on AT, it reduced the normal-state false-alarm rate from 0.2854 to 0.1646 while maintaining 0.9708 fault sensitivity. Raw-only inference required 0.266 ms per slice. WPMSR-1D-ResNet, therefore, aligns wavelet augmentation with diagnostic performance, preserves useful waveform information, and removes wavelet reconstruction and PSO from the deployment path.
This paper proposes a highly efficient framework, the Frequency-domain Circulant Attention Vision Transformer (FC-ViT), for robust rotating machinery monitoring, and demonstrates superior noise immunity and cross-load generalization.
Zhijun Teng, Ji-Qiu Li, Mingyang Sun et al.· IEEE Access· 0 citations
An unsupervised hybrid deep learning framework for unknown bearing fault diagnosis and severity assessment using vibration signals that combines Continuous Wavelet Transform, Convolutional Neural Networks, and Long Short-Term Memory autoencoders is presented.
Edris Shamsulhaq, Fikri Arif Wicaksana· Jambura Journal of Electrica...· 0 citations
Fault diagnosis of rolling element bearings (REBs) is crucial for ensuring the safety, reliability, and economic efficiency of modern industrial systems. However, conventional deep learning models often suffer from high computational costs and fixed receptive fields, which limit their deployment on resource-constrained edge devices. To address these issues, an adaptive variable-scale lightweight convolutional neural network (AVS-LCNN) is proposed. First, Gramian Angular Difference Field (GADF) coding is employed to transform one-dimensional vibration signals into time-frequency dual-channel images. Subsequently, depth-separable convolution is utilized to reconstruct the backbone network of AVS-LCNN, significantly reducing the number of model's parameters. To better capture the multi-scale characteristics of fault signals, a variable-scale feature extraction mechanism is developed based on the dilated convolution and the Atrous Spatial Pyramid Pooling (ASPP) module. Additionally, a sample-aware dynamic weighting strategy is introduced, in which a soft gating mechanism generates a weights α, enabling the model to automatically optimize its structure according to the complexity of the input samples. Experiments conducted on the HTBF and PU datasets show that AVS-LCNN achieves a diagnostic accuracy rate of over 99% with only 0.17M parameters, demonstrating a favorable balance among computational accuracy, robustness, and inference efficiency. These results indicate that the proposed method provide an effective solution for industrial edge applications.
Jia-Dong Meng, Zhao-An Hao, Hu-Tang Sang et al.· Measurement science and tech...· 0 citations
