Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 37 references
Medicine
TL;DR
Experiments indicate that the proposed fault diagnosis approach that combines Continuous Wavelet Transform, Convolutional Neural Network, CNN, Black-winged Kite Algorithm, and Least Squares Support Vector Machine outperforms CNN, CNN-SVM, and CNN-BiGRU under small-sample conditions.
Abstract
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).
A method based on refined composite multi-scale attention entropy (RCMAE) and Beluga whale optimization (BWO) for multi-classification support vector machines (SVM) to solve problems of poor feature discriminability and blind parameter selection in traditional fault diagnosis is proposed.
Bing Wang, Huimin Li, Xiong Hu· Journal of Vibration and Con...· 0 citations
To address the issues of rolling bearing fault vibration signals being susceptible to noise interference and the support vector machine (SVM) relying on manual parameter settings, this paper proposes a fault diagnosis method based on ICEEMDAN and AIHO−SVM. Firstly, the Hippopotamus Optimization Algorithm is improved by incorporating Chebyshev chaotic mapping, refraction opposite learning, dynamic weighting, adaptive step size, and guided learning strategies, thereby enhancing convergence accuracy and speed. Secondly, ICEEMDAN is employed to decompose vibration signals for noise reduction, and the effective intrinsic mode function (IMF) components are selected according to the mutual information criterion to reconstruct the signal. Nine time−domain statistical features are then extracted to construct the fault feature vector. Thirdly, AIHO is used to collaboratively optimize the penalty factor and kernel parameters of SVM, establishing an AIHO−SVM classification model. Finally, the proposed method is validated on bearing datasets from Case Western Reserve University and Huazhong University of Science and Technology. Experimental results show that the average diagnostic accuracies on the two datasets reach 99.07% and 99.09%, respectively, demonstrating the effectiveness of the proposed method for rolling bearing fault diagnosis.
Liping Wang, Yao-Zheng Zhao, Yan Chen et al.· Information· 0 citations
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
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.
Taoyang Zhan, Kang Han, Yuhan Huang et al.· Insight - Non-Destructive Te...· 0 citations
To address the problems of severe feature coupling, difficult fault information extraction, and insufficient recognition accuracy for cylindrical roller bearings under multiple fault conditions, this paper proposes a multi-fault pattern recognition method based on a Northern Goshawk Optimization algorithm improved by refraction opposition-based learning and the sine–cosine algorithm (RSNGO). The RSNGO is used to optimize variational mode decomposition (VMD) and a convolutional neural network–bidirectional long short-term memory–self-attention (CNN–BiLSTM–SAT) network. First, RSNGO adaptively optimizes the number of decomposition modes and the penalty factor of VMD, and selects the optimal intrinsic mode function (IMF) components, from which time-domain statistical features are extracted to construct the sample set. Then, a CNN–BiLSTM–SAT diagnostic network is constructed, and RSNGO is employed to jointly optimize its key hyperparameters, including convolution kernel size, number of convolution kernels, number of BiLSTM hidden units, and initial learning rate. In this network, CNN extracts local features, BiLSTM models temporal dependencies, and the self-attention mechanism enhances the representation of critical fault features. Finally, the constructed feature samples are input into the optimized network to realize multi-fault pattern recognition of cylindrical roller bearings. Experimental results demonstrate that the proposed method effectively improves the separability and recognition accuracy of multi-fault features, exhibits strong robustness and generalization capability under complex operating conditions, and provides an effective solution for intelligent bearing fault diagnosis.
Lihai Chen, Zhenshui Li, Ao Tan et al.· Machines· 0 citations
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