TL;DR
A fault recognition model combining convolutional neural network and Bayesian optimization (BO)-XGBoost, constructed by extracting time–frequency domain and fusion features from dual-branch CNN and identifying faults solves the problem of data imbalance.
To effectively extract fault features from motor bearings and improve the accuracy of fault diagnosis, this paper proposes a novel method for motor bearing fault diagnosis based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Golden Jackal Optimization (GJO) to optimize Convolutional...
Experiments show that the proposed adaptive variable-scale lightweight convolutional neural network (AVS-LCNN) achieves a diagnostic accuracy rate of over 99% with only 0.17 M parameters, demonstrating a favorable balance among computational accuracy, robustness and inference efficiency.
A fault-diagnosis method that integrates a least-squares-loss and weight-normalization deep convolutional generative adversarial network (LW-DCGAN) with a convolutional-attention-enhanced 34-layer residual network (C-ResNet34) to improve fault-feature extraction and demonstrate the effectiveness of the proposed method.
This paper introduces a comprehensive leakage-free methodology for bearing fault diagnosis by three of the most representative feature learning approaches: Raw Signal Convolutional Neural Network (Raw CNN), Fast Fourier Transform (FFT) based hybrid MLP-CNN, and handcrafted statistical descriptor based Multilayer Percep...
Rolling bearings are the vital support elements of industrial rotating machinery. Operational conditions have a direct impact on the safety, dependability and economical operation and maintenance of the production system. An effective and accurate fault diagnosis technology is very important for predictive maintenance...
A bearing fault diagnosis method based on the Dual Attention Multi-Scale Convolutional Neural Network that achieves a peak diagnostic accuracy of 99.69% in cross-condition tasks and maintains accuracy above 92% under −5 dB SNR, outperforming BiTCN-Transformer, MTFC-T, and CTCA in accuracy, recall, and F1-score, while a...
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