Jul 2026· Measurement science and technology· Vol 37, pp. 336104· 0 citations· 34 references
Physics
Abstract
Bearing fault diagnosis is a key strategy to ensure the stability of mechanical systems, optimize maintenance plans and improve operational reliability. Vibration signals are complex time series with unique properties. Most of the current methods only consider the spatial characteristics of the signal, but do not take into account its temporal characteristics. In fact, vibration signals contain both spatial and temporal information, offering not only rich temporal dynamic details but also spatial structural insights that reflect fault characteristics. Therefore, in order to fully mine and integrate the spatiotemporal feature information in vibration signals to enhance the accuracy and robustness of intelligent diagnosis, we propose a bearing fault diagnosis method based on the fusion of spatial and temporal features. Firstly, an improved wide kernel deep convolutional neural network method was proposed. By using an adaptive channel module, the model was enabled to focus on the key features of the vibration signal and suppress the interference of irrelevant information. At the same time, the superimposed bidirectional long short-term memory layer and the gated recurrent unit effectively capture the long-term dependencies of the time series in the dataset, covering both past and future signal features. This method can effectively utilize temporal information and combine it with spatial features, significantly improving the accuracy and robustness of bearing diagnosis. To verify the proposed method, ablation and comparative experiments were conducted on two publicly available bearing datasets: the Case Western Reserve University dataset and the Guangdong University of Petrochemical Technology dataset. The experimental results show that the average accuracy rates of fault diagnosis of the WBLG network on the two sets of datasets have reached 99.7% and 96.5% respectively. Compared with the existing models, the maximum improvement rates are 3.73% and 12.4% respectively. It demonstrates its superior classification performance and generalization applicable to bearing fault diagnosis.
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
DaXin Li, Hong Wang, Hai Xue et al.· Engineering Research Express· 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
A dual-channel attention and feature selection-based fault diagnosis method for rolling bearings is proposed, and a corresponding digital twin-based interaction system is further developed.
Erpeng Wang, Zhaoze Sun, Jian Wang et al.· Engineering Research Express· 0 citations
Multimodal fault diagnosis has attracted widespread attention in the field of rotating machinery due to the complementary information provided by multiple sensor signals. However, the design of signal fusion networks and the extraction of correlation features between multiple signals remain challenging. To address this, this paper proposes a multimodal framework based on differentiable architecture search to fuse vibration and acoustic emission signals, two signals of vastly different orders of magnitude, and to achieve automatic search for the fusion network. Simultaneously, a cross-modal dynamic convolutional module is introduced to extract and fuse correlation features between multiple signals. Experimental results on two benchmark gear fault datasets demonstrate that, compared to traditional models and existing network search methods, the proposed method exhibits stronger robustness under different operating conditions and achieves superior diagnostic performance. Ablation experiments further validate the effectiveness of the proposed interaction mechanism and joint optimization strategy.
Jialin Li, Kun Long, Yong Xiao et al.· Engineering Research Express· 0 citations
A novel diagnosis method integrating FFT-VMD feature extraction with a Bi-TCN-Bi-GRU neural network, which maintains an exceptional diagnostic accuracy even under severe background noise.
A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.
Canan Taştimur· Information Technology and C...· 0 citations
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