Rolling Bearing Fault Diagnosis Method Based on RSBU-MSCNN under Strong Background Noise

A Fault Diagnosis Framework for Rolling Bearings Based on PPCA-AR Anti-Interference Preprocessing and LSTM

Prevailing rolling bearing fault diagnosis frameworks based on long short-term memory (LSTM) are susceptible to noise interference under industrial strong-noise working conditions, suffering from insufficient feature extraction capability and low diagnostic precision. To address these limitations, this paper proposes a fault diagnosis framework integrating deep learning with signal processing, which consists of probabilistic principal component analysis (PPCA) for noise suppression, the autoregressive (AR) model for discrete interference elimination, spectral kurtosis (SK) for fault feature enhancement, and LSTM-based intelligent classification. To improve the signal-to-noise ratio (SNR) of vibration signals, the proposed method first estimates and suppresses noise via PPCA, and then eliminates periodic discrete frequency interferences represented by gear meshing components using the AR model. Following interference suppression, the SK method is adopted to implement multi-scale resonant frequency band screening and envelope demodulation. Finally, the demodulated features are learned by the LSTM to realize intelligent fault diagnosis of rolling bearings. This novel approach not only improves fault diagnosis accuracy but also enhances the model interpretability with the aid of signal processing techniques. Experimental results on the Case Western Reserve University (CWRU) and industrial field datasets demonstrate that the proposed method achieves superior accuracy compared with state-of-the-art approaches under various SNR conditions. It effectively mitigates the accuracy degradation of deep learning diagnostic models in strong-noise environments, providing a reliable technical solution for the intelligent diagnosis of rolling bearings.

Fault Diagnosis of Rolling Bearings Based on CWT- RT Wavelet Scattering Networks

Aiming at the non-stationary, nonlinear and noise-sensitive characteristics of rolling bearing vibration signals, as well as the low recognition accuracy of traditional deep learning in small-sample scenarios, this paper proposes a rolling bearing fault diagnosis method combining Continuous Wavelet Transform with Ridge Tracking (CWT-RT) and Multi-Scale Wavelet Scattering Network. First, Variational Mode Decomposition integrated with Cramer Von Misses statistic (VMD-CVM) is adopted to denoise the original signal and improve the signal-to-noise ratio. Then, CWT-RT is used to transform the denoised signal into time-frequency spectrograms for intuitive time-frequency feature representation. Multi-Scale Wavelet Scattering Network is further applied to extract multi-level structural features, which are fed into Multi-Layer Perceptron (MLP) to realize bearing fault identification. To eliminate data leakage, all original raw vibration files are split into training and test sets at a 7:3 ratio before sliding window sampling. Validation experiments on bearing datasets from South Ural State University, CWRU, and XJTU-SY show that the diagnostic accuracies on two small-sample conditions reach 98.78% and 98.09%, respectively. the 95% confidence intervals for the two accuracy values are [98.21%, 99.15%] and [97.43%, 98.67%], respectively. Across 10 repeated experiments, p-values < 0.001 confirm the statistical significance of the results. The model maintains high accuracy under different loads and noise levels (0/5/10/15 dB). Comparative and ablation experiments verify that the method has high diagnostic accuracy, strong noise robustness and superior small-sample learning ability, with each module effective, and the full-pipeline latency meets the real-time requirements of IoT edge deployment, providing support for industrial engineering applications.

Adaptive smoothed soft-threshold method for noise-robust bearing fault diagnosis

Rolling bearing fault diagnosis based on time-frequency representation and multi-scale feature learning

A rolling bearing fault diagnosis method based on multi-scale depthwise separable convolution (DSC) 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 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.

An improved CNN–Transformer–ELM framework with residual shrinkage networks for bearing fault diagnosis

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.

A CNN Feature Extraction and BKA-Optimized LSSVM Classification Method for Small-Sample Rolling Bearing Fault Diagnosis

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.