Sparse Impulse Feature Learning for Bearing Fault Extraction

Periodic sparse joint deconvolution and its application to bearing fault detection

Rolling bearing fault detection under strong background noise remains a challenging task because fault-induced repetitive transients are usually weakened by the transmission path and masked by random interference. To address this issue, this paper proposes a periodic sparse joint deconvolution (PSJD) method for extracting weak bearing fault impulses from vibration measurements. The proposed method formulates bearing fault detection as an inverse filtering problem, in which the deconvolved signal is expected to exhibit both strong periodicity and high sparsity. Specifically, a multi-period correlation term is constructed to enhance repetitive impulses occurring at the fault characteristic period, while a logarithmic sparsity term is introduced to promote impulsive structures and suppress noise-related components. In addition, a regularization term is imposed on the inverse filter to improve numerical stability and avoid excessive oscillation of filter coefficients. The inverse filter is updated using a gradient-ascent scheme with normalization to remove scale ambiguity. The effectiveness of the proposed method is verified using simulated signals and experimental bearing vibration signals. The results demonstrate that the proposed method can effectively recover weak periodic transients and highlight fault characteristic frequencies in the envelope spectrum, even when the original signal is contaminated by strong noise. Compared with conventional deconvolution methods, the proposed method provides better capability in enhancing repetitive impulsive features and improving the reliability of bearing fault identification.

PGDS-CLNet: A robust bearing fault diagnosis method under varying loads and strong noise

A novel intelligent fault diagnosis framework, termed PGDS-CLNet, is proposed in this study, which is an end-to-end trainable diagnostic network after standard signal normalization and segmentation.

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

A lightweight convolutional neural network method for bearing fault diagnosis based on dynamic weighting and variable-scale feature extraction

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.

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.

Noise-Robust Multi-Domain Feature Integration for Bearing Fault Diagnosis

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.