Aug 2026· Journal of engineering and applied sciences· Vol 73· 0 citations· 42 references
TL;DR
The proposed framework provides an effective balance between diagnostic accuracy, robustness, interpretability, and computational efficiency, making it a promising solution for intelligent condition monitoring and predictive maintenance of rotating machinery.
Abstract
This paper presents a systematic framework for real-time fault-type identification in rolling element bearings using vibration signal analysis. The Case Western Reserve University bearing dataset is employed as the primary data source, comprising vibration signals recorded under different load conditions (0–3 HP). Initially, Envelope Analysis (EA) is applied to extract fault-related characteristic frequencies. While computationally efficient, EA successfully identifies fault features in 66.67% of the signals but shows limitations under noisy and spectrally smeared conditions. To address this, Cepstrum Prewhitening Analysis (CPA) is selectively applied to unresolved signals, achieving a 75% detection success in these cases and improving the overall detection rate to 91.67%. Thereafter, 36 time-domain, frequency-domain, and EA-CPA based features were extracted from segmented vibration signals. A sequential feature optimization strategy comprising variance threshold filtering, correlation analysis, Z-score normalization, ANOVA F-test feature ranking, and Recursive Feature Elimination reduced the feature set to the 10 most discriminative features. To eliminate sample- and group-level information leakage, a leakage-free Nested GroupKFold framework was developed, in which preprocessing, feature selection, and hyperparameter optimization was performed exclusively within the training folds using GridSearchCV. Six machine learning classifiers, namely Random Forest, XGBoost, LightGBM, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression, were comparatively evaluated. XGBoost achieved the highest mean classification accuracy of 97.62%, while RF attained a comparable accuracy of 97.51% with lower fold-to-fold variation, indicating superior robustness and stability. Consequently, RF was selected for independent cross-condition validation, in which it was trained solely on the 0 HP operating condition and evaluated on the unseen 1 HP, 2 HP, and 3 HP datasets, demonstrating strong generalization across varying load conditions. Feature importance analysis further confirmed that the characteristic bearing defect frequencies (BPFO, BPFI, and BSF) are the dominant contributors to classification performance. The proposed framework provides an effective balance between diagnostic accuracy, robustness, interpretability, and computational efficiency, making it a promising solution for intelligent condition monitoring and predictive maintenance of rotating machinery.
Vibration signals in rotating machinery are often complex, with fault-induced pulses masked by noise and coupled under compound fault conditions, which increases diagnostic difficulty. Although Feature Mode Decomposition can analyze non-stationary signals, its performance is limited by empirical parameter settings, especially filter length and mode number.A parameter-adaptive framework named EPFMD is developed to address this issue. It optimizes key parameters using a composite health indicator that combines envelope entropy and pulse factor, enabling accurate characterization of fault features. The Ivy Algorithm is applied for automatic parameter optimization. A fusion evaluation index based on kurtosis and pulse factor is then used to select the most fault-sensitive component, followed by envelope demodulation for feature extraction. Validation on the CWRU dataset and experimental data demonstrates that the proposed method effectively identifies inner race, outer race, and compound faults, showing superior performance compared with existing methods.
Xing-Ru Pan, Zhi-Lin Peng· Eksploatacja I Niezawodnosc-...· 0 citations
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.
Weak fault modulation components in motor stator current signals are easily masked by dominant low-frequency components, and redundant multi-domain features can further degrade bearing fault classification. To address these problems, this paper proposes a feature selection method based on an adaptive binary grey wolf optimizer (ABGWO). Feature reconstruction (FR) is first used to suppress the dominant low-frequency component of the current signal, and time-domain, frequency-domain, spectral-peak and wavelet packet energy features are extracted from the residual signal. On this basis, a nonlinear convergence factor, an adaptive population update mechanism and a feature-number-penalized objective function are introduced into BGWO to improve the fixed search schedule, susceptibility to local convergence and redundant feature retention. The selected features are then fed into XGBoost for bearing state identification. Under the baseline operating condition, the complete ABGWO selects six features from the 36-dimensional candidate feature set, achieving a feature compression rate of 83.33%, an average test accuracy of 99.63% and a Macro-F1 of 99.62%. On the self-measured data set, the proposed method achieves an average accuracy above 99% under all four operating conditions C1-C4. Further validation on the Paderborn University public data set with real bearing damage shows an average accuracy above 98.7% under different damage severities and operating conditions. The results indicate that the proposed method effectively reduces feature redundancy while maintaining high diagnostic performance across different data sources and operating conditions.
Hai-Tao Liang, Guo-Fu Li· Engineering Research Express· 0 citations
Slewing bearings in low-speed, heavy-load equipment generate weak and heterogeneous fault signatures that are difficult to characterize using a single sensor. This study develops a compact dual-branch feature-fusion framework that jointly exploits six-channel vibration and one-channel acoustic-emission (AE) signals. To prevent source-record leakage, complete raw recording groups are assigned to training, validation, and test subsets before segmentation; non-overlapping 1024-point windows are then generated, and channel normalization is fitted using training groups only. Five matched random-seed runs are performed, with the best checkpoint selected exclusively by validation Macro-F1. The fusion model achieves mean test accuracy of 99.10% and Macro-F1 of 0.9910, compared with 97.38%/0.9738 for vibration-only and 98.03%/0.9802 for AE-only. The improvement over vibration-only is statistically significant (p = 0.022 for both Accuracy and Macro-F1), whereas the improvement over AE-only is numerical but does not reach the 0.05 significance level (p = 0.063). The fusion model also obtains the lowest mean Davies-Bouldin index (0.963). These results support compact vibration-AE fusion as an effective diagnostic baseline while also defining its statistical and deployment limitations.
Reliable fault diagnosis in rotating machinery is challenging due to the nonlinear and non-stationary nature of vibration signals. Although time–frequency analysis is widely used, it cannot capture the cross-scale coupling between amplitude-modulated (AM) and frequency-modulated (FM) components that carry essential diagnostic information. This study applies Holo-Hilbert Spectrum Analysis (HHSA) to extract amplitude–frequency modulation features and integrates them with six machine learning classifiers to identify four fault conditions. Random Forest, K-Nearest Neighbors, and Logistic Regression achieve accuracies of up to 99.95%, yielding higher accuracy than Fast Fourier Transform-based features. The proposed framework employs an HHSA-based feature extraction pipeline that effectively captures AM–FM coupling in nonlinear vibration signals. It also provides higher discriminative capability than traditional spectral approaches and maintains robustness across multiple classifiers. This method offers high diagnostic accuracy and strong potential for industrial predictive maintenance. Future work will focus on improving computational efficiency and evaluating the framework under more diverse and realistic operating conditions.
Received: 10 September 2025 | Revised: 20 April 2026 | Accepted: 10 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The VBL-VA001 datasets that support the findings of this study are openly available at https://doi.org/10.1007/s42417-023-00959-9, reference number [44].
Author Contribution Statement
Van-Trung Nguyen: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Ba-Tan Le: Investigation, Data curation, Writing - original draft, Writing - review & editing. Van-Phuong Dao: Methodology, Validation, Writing - review & editing.
Van-Trung Nguyen, Ba-Tan Le, van-Phuong Dao· Journal of Computational and...· 0 citations
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.
Hai Ling, Lufan Wang, Wen Liu et al.· Information Technology and C...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
