Aug 2026· Insight - Non-Destructive Testing and Condition Monitoring· 0 citations· 3 references
TL;DR
The validation results on multiple typical bearing fault datasets show that the proposed MorletConv CNN model is characterised by enhanced physical interpretability and generalisation ability while maintaining high diagnostic accuracy, providing new ideas and method support for achieving highly reliable rolling bearing fault diagnosis.
Abstract
As a key component in mechanical equipment, the operating status of rolling bearings directly affects the stability and performance of the entire system. Existing mainstream fault diagnosis methods using vibration signals are primarily based on ‘black box’ models. Although
they are characterised by high recognition accuracy, the decision-making basis and interpretability are insufficient, making it challenging to meet the needs of high-reliability application scenarios. Therefore, an interpretable Morlet convolutional neural network (MorletConv CNN) model based
on Morlet convolution is proposed in this paper to reveal the inherent mechanism by which the model identifies fault features. It is difficult to extract features adaptively with traditional signal processing methods. Furthermore, the decision logic of convolutional neural networks (CNNs)
is opaque. As a result, a Morlet preprocessing layer, embedded into the CNN structure, is designed in this paper. Combined with prior knowledge of time-frequency analysis, the proposed layer enables the model to learn key structural features in vibration signals more effectively, improving
the accuracy and reliability of fault recognition. Specifically, the proposed model utilises two convolutional kernel branches, one for the real part and one for the imaginary part, to enhance sensitivity to minor signal changes and improve its ability to detect early faults. Simultaneously,
the control parameter θ of the Morlet kernel function is set as a trainable variable to reduce model complexity and accelerate training. This function automatically aligns key frequency components during the optimisation process, improving the time-frequency feature extraction ability
of the model. The validation results on multiple typical bearing fault datasets show that the proposed MorletConv CNN model is characterised by enhanced physical interpretability and generalisation ability while maintaining high diagnostic accuracy, providing new ideas and method support for
achieving highly reliable rolling bearing fault diagnosis.
Motor Current Signature Analysis (MCSA) is a non-invasive technique that enables the detection of bearing faults in rotating electrical machines without the need for additional sensors. In this study, the Paderborn University bearing dataset was utilized to perform a two-stage analysis. In the first stage, motor current data were processed directly using 1-Dimensional Convolutional Neural Networks (1D-CNN). In the second stage, scalogram images obtained via Continuous Wavelet Transform (CWT) were used to train five different deep learning models, with the ResNet18-based 2D-CNN model providing the best performance. To prevent data leakage, the training and testing sets were partitioned based on individual bearings to ensure complete isolation. The experimental results demonstrated that both 1D-CNN and ResNet18-based 2D-CNN methods achieved 100% accuracy in detecting outer race faults. However, it was observed that the impact of inner race faults on the stator current remains weak due to the complex physical transmission path of the fault signal, resulting in significantly lower detection rates.
Y. Çekiç, Aydin Akan· Signal Processing and Commun...· 0 citations
Rolling bearings are indispensable elements in mechanical equipment, and their condition is closely related to system reliability and operational safety. To enhance diagnostic performance with limited samples and reduce the dependence on manual parameter selection, this study proposes a fault diagnosis approach that combines Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN), Black-winged Kite Algorithm (BKA), and Least Squares Support Vector Machine (LSSVM). The original 1-D vibration signals are first processed by CWT to obtain 2-D time–frequency representations. CNN is then employed to learn deep fault-sensitive features, which are subsequently fed into LSSVM for state classification. To further improve classification performance, BKA is used to automatically search for the optimal LSSVM parameters, with validation accuracy adopted as the fitness criterion. Experiments conducted on three public bearing datasets, namely CWRU, JNU, and SEU, indicate that the proposed method outperforms CNN, CNN-SVM, and CNN-BiGRU under small-sample conditions. In addition, t-SNE results show more distinct feature clusters, while BKA exhibits faster convergence and better global search capability than Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).
Shi-Yan Sun, Yujun Shi, Quan Li et al.· Italian National Conference...· 0 citations
To address the challenges of difficult fault feature extraction and feature aliasing in aero-engine inter-shaft bearings under strong noise conditions, this paper proposes a fusion diagnostic method that integrates a dual-scale one-dimensional convolutional neural network, a multi-head self-attention Transformer, and a bidirectional gated recurrent unit. The method employs a three-stage progressive network architecture for end-to-end fault diagnosis. The dual-scale 1DCNN extracts local temporal features from vibration signals, and batch normalization and dropout are applied to stabilize training and reduce potential overfitting. The Transformer encoder models dependencies among the extracted feature representations, supporting the representation of fault-sensitive features. The BiGRU captures bidirectional temporal dependencies in the fault evolution process. Experimental validation on the Harbin Institute of Technology aero-engine inter-shaft bearing dataset shows that the proposed model achieves 97% diagnostic accuracy under extreme noise conditions (SNR = -5 dB). Compared with existing methods, these results indicate that the proposed network effectively maintains diagnostic performance under controlled noise conditions.
Yang Wang, Boliang Zhang· Scientific Reports· 0 citations
Rolling bearings are essential components in mechanical systems, whose fault diagnosis is vital for operational efficiency. But in real industrial environments, the harsh conditions, including noise, missing data, and compound faults, severely limit the diagnostic performance of the algorithm. Thus, we propose an ensemble attention-based residual convolutional neural network (CNN) optimized by the vortex search algorithm. First, a new residual CNN with the improved residual structure, the separable convolution, and the global average pooling layer is designed to extract features from the vibration signals automatically. Second, a residual cooperative attention mechanism is presented. To guarantee the difference between the base models, different base models are constructed employing multiple convolutional kernels, activation functions, as well as attention mechanisms, respectively. And different training sets are allocated to each base model by Bootstrap. Third, a new exponential threshold decision fusion strategy is put forward to achieve ensemble learning. Eventually, the vortex search algorithm is employed to optimize the parameters of the decision fusion strategy. The noise, missing data, and compound fault datasets constructed separately using data from two rolling bearing experiments reveal that the proposed ensemble model can effectively overcome the limitations of individual models and achieve superior fault identification performance than existing methods under many types of severe conditions.
A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (MDSC) and a convolutional neural network–Transformer hybrid model (CNN-Transformer) is proposed to address the non-stationarity of fault signals and the difficulty of jointly capturing local and global features. First, continuous wavelet transform (CWT) converts one-dimensional vibration signals into two-dimensional time-frequency images to enhance fault representation. Then, multi-scale convolution (MSC) and depthwise separable convolution (DSC) are introduced to extract local impulsive features and fault patterns at different scales with fewer parameters. A CNN-Transformer architecture is further developed, where convolutional neural network (CNN) captures local details and Transformer models global dependencies. In addition, pretraining-finetuning, data augmentation, label smoothing, and normal sample optimization are adopted to improve training stability and diagnostic performance. Experimental results show accuracies of 98.80% on the Xi’an Jiaotong University bearing dataset (XJTU-SY) and 100.00% on the Case Western Reserve University bearing dataset (CWRU), demonstrating strong discriminative ability, stability, and robustness.
Shuai Yang, Yanchao Chen, Yang Yu· Engineering Research Express· 0 citations
Rolling bearings are key elements in rotating machinery, and reliable fault diagnosis is crucial for condition monitoring and maintenance decisions. Under strong background noise, vibration signals are easily distorted, which degrades conventional CNN-based diagnosis. To address this issue, an RSBU-MSCNN-based approach is proposed. First, Gaussian white noise with different signal-to-noise ratios is added to original signals to simulate industrial noise, and one-dimensional vibration signals are transformed into two-dimensional time–frequency representations using CWT. Then, a residual shrinkage module with a soft-threshold function is introduced for adaptive denoising and redundant noise suppression, while multi-channel, multi-scale convolutions enhance robust feature extraction across different receptive fields. Finally, faults are classified using fully connected layers. Experiments on multiple datasets show high accuracy under strong noise, confirming the robustness and applicability of the proposed method for industrial maintenance.
Zhe Zhang, Qianlu Guo, Dong Liu et al.· Eksploatacja I Niezawodnosc-...· 0 citations