Jul 2026· Nigerian Journal of Technology· 0 citations· 21 references
TL;DR
The results underscore the effectiveness of incorporating the Fourier activation function into machine learning models, as it enhances the ability to capture complex fault dynamics and improves diagnostic accuracy in industrial applications.
Abstract
Accurate diagnosis of faults in rotating industrial machinery is very critical for operational efficiency, reducing downtime, and avoiding costly failures and the associated economic losses. This study makes use of a hybrid machine learning model designed to classify known fault types which include imbalance, overhang, underhang (bearing), and horizontal faults based on vibration signal data. Traditional hybrid models often struggle to capture the complex, oscillatory nature of vibration data using standard activation functions like ReLU, which can lead to poor representation of non-linear and periodic fault dynamics. The novelty in the proposed approach is the incorporation of the Fourier function as an activation function within the model architecture. This models periodic and non-linear behaviors, enhancing the detection of subtle patterns missed by standard functions. This function captures both periodic and non-linear behaviours in the data, enhancing the model’s ability to distinguish between subtle fault patterns that traditional activation functions might overlook. The model's performance was evaluated using cross-validation, which revealed high precision, recall, and F1-scores (all above 0.90) for imbalance and bearing faults. However, horizontal faults posed more classification challenges, with F1-scores ranging from 0.73 to 0.76, likely due to spectral overlaps and noise. Among the classifiers assessed, XGBoost achieved the lowest error rate at 0.0067, followed closely by Random Forest at 0.007 and CNN at 0.008. Notably, the hybrid model outperformed existing methods, such as Abbasi’s approach, which achieved 92% accuracy, by reaching a peak accuracy of 99% in fold three. These results underscore the effectiveness of incorporating the Fourier activation function into machine learning models, as it enhances the ability to capture complex fault dynamics and improves diagnostic accuracy in industrial applications.
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
Experimental results indicate that XGBoost achieves the highest accuracy in identifying unbalance, outperforming the neural network and the Bayesian model and for misalignment detection, however, the three methods exhibit comparable performance, underscoring the limitations of ML models that rely solely on vibration indicators for this fault type.
A. Marzougui, A. Hachem, T. Mazoyer· Insight - Non-Destructive Te...· 0 citations
Anti-friction bearings are fundamental components in rotating machinery. Any bearing fault appearing during operation could lead to catastrophic damages and failures without proper maintenance. Numerous methods have been developed for bearing fault detection to reduce maintenance costs and avoid unscheduled downtime. However, once a bearing fault is detected, assessing defect severity may be of more critical concern to industries, as it determines the urgency of interventions such as replacement scheduling and maintenance strategies. This paper presents an efficient estimation method for bearing fault severity using vibration-based input parameters and machine learning. Based on modal characteristics, key input parameters, the vibration amplitudes at the bearing fault frequencies and their harmonics, are extracted from acceleration envelope spectra for their close correlations with physical defect conditions. The nonlinearity between these spectral parameters and bearing fault severity is revealed with experimental observations and is represented using artificial neural networks. The model is validated on experimental vibration data measured from a bearing rig, covering various defect scenarios of different sizes and shapes. The classification criteria of bearing fault severity levels, ranging from healthy to severe, are formulated based on physical defect sizes with maintenance recommendations. Robust and accurate fault severity estimation is achieved across three bearing datasets collected under different operating conditions. The proposed method addresses both fault detection and degradation assessment for anti-friction bearings using simple vibration-based parameters based on rotor and bearing dynamics, providing a practical framework for predictive maintenance in industrial applications.
Haobin Wen, Khalid M. Almutairi, Jyoti K. Sinha et al.· Machines· 0 citations
Early detection of rolling bearing faults is essential for preventing failures in rotating machinery. Although vibration-based analysis is widely used for fault diagnosis, it requires additional sensors, such as accelerometers, which increases system cost and complexity. Motor current signals, inherently available in industrial drive systems, offer a cost-effective alternative; however, their indirect sensitivity to mechanical defects makes reliable fault diagnosis challenging. This paper proposes a hierarchical motor current-based diagnostic framework that integrates domain knowledge with data-driven image analysis. Raw current signals are transformed into time-frequency spectrograms, which provide enhanced representations of faults compared to pure time or frequency-domain features. The proposed approach consists of three stages. First, bearing health is characterized using a rule-based method to compute the characteristic frequencies associated with different fault types, incorporating domain knowledge into the diagnosis process. Second, this information is used to guide a hierarchical classification strategy that separates health assessment from fault localization. Finally, global image descriptors extracted from the spectrograms are employed to classify the bearing condition as healthy or faulty and to identify the fault location (internal or external). By explicitly combining physics-informed fault characterization with image-based machine learning, the proposed approach improves interpretability while maintaining competitive diagnostic performance. The effectiveness of the method is validated on a benchmark bearing dataset, demonstrating its potential for cost-effective industrial condition monitoring.
Arantzazu Florez, G. Echegaray, A. M. Florez-Tapia et al.· International Journal of Com...· 0 citations
A new predictive maintenance model that incorporates multi-domain feature extraction, and hybrid feature selection approach and optimized ensemble classifier to facilitate high-fidelity fault detection in four operational modes is proposed, affirming its suitability for deployment in industrial cyber-physical monitoring systems.
Premsagar D Patil· Materials Research Proceedin...· 0 citations
The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.
A. Oner, Meral Bayraktar· Italian National Conference...· 0 citations