Aug 2026· Italian National Conference on Sensors· Vol 26· 0 citations· 39 references
Medicine
TL;DR
A progressive three-stage time–frequency learning framework that identifies series arc faults directly from normalized current waveforms and provides robust discrimination across unseen measurement sessions within the evaluated load categories and operating conditions is presented.
Abstract
Arc faults in low-voltage smart-farm distribution systems are difficult to detect reliably because normal current waveforms vary with load composition and operating conditions. This paper presents a progressive three-stage time–frequency learning framework that identifies series arc faults directly from normalized current waveforms. In Stage I, fast Fourier transform (FFT)-based and wavelet-based learnable front-ends independently learn complementary global spectral and local transient representations. In Stage II, their representation spaces are structured using supervised contrastive learning with view-level and cross-spectrum alignment. In Stage III, the aligned feature extractors are frozen, and only the feature fusion block and final classifier are trained for 10-class classification. Current signals are acquired from a practical smart-farm distribution system and evaluated using a measurement-session-level group-wise split. The framework achieves 99.92% accuracy and 99.93% macro recall while maintaining 1.06 M parameters and a model-only inference latency of 8.70 ms. The results demonstrate that the proposed framework provides robust discrimination across unseen measurement sessions within the evaluated load categories and operating conditions.
A hybrid two-stage machine learning pipeline that decouples detection from classification is proposed, and the direction of the zero-sequence signature is found to be system-dependent, motivating a learned decision boundary in place of a fixed relay threshold.
Sahil Manikshete, A. Gujarathi, Thanh Long Vu et al.· 0 citations
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
The proposed WT-Transformer fault diagnosis model achieves superior performance in track circuit fault diagnosis, especially in the classification of rare faults with few samples, and the effectiveness of wavelet transformation and time-frequency feature enhancement is verified.
Yi Shi, Xuechun Ge, Qizheng Hu et al.· Measurement and control (Lon...· 1 citation
Rotating machinery plays a critical role in transmission systems, while the scarcity of fault samples and labeled data limits the performance of existing diagnostic methods under few-shot conditions. This paper proposes a few-shot fault diagnosis method for rotating machinery based on a time-frequency dual-stream Mamba architecture and joint metric learning. Raw vibration signals and multi-scale short-time Fourier transform time-frequency views are constructed to characterize transient impacts and frequency-band energy distributions from complementary perspectives. A dual-stream feature extraction network is developed, where the time-domain branch captures impulsive fault patterns, and the time-frequency spatial Mamba and channel attention branches extract spatial dependencies and fault-sensitive responses. Moreover, a hierarchical selective fusion mechanism is introduced to adaptively integrate complementary features across different domains. Finally, a covariance-based joint metric learning module is designed to model class distributions using second-order statistics of support samples and classify query samples through local similarity aggregation. Experimental results show that the proposed method consistently outperforms comparison methods under different rotational speeds, training sample scales, and 8-way 1-shot/5-shot tasks, validating its effectiveness and robustness for few-shot rotating machinery fault diagnosis.
A new hybrid approach to fault detection in power systems based on S-Transform feature extraction and SVM-based intelligent classification is proposed that can clearly distinguish between power swings and actual faults, including symmetrical three-phase faults that have characteristics similar to power swings.
P. Sharma, M. Silas, P. Roy et al.· African Journal Of Applied R...· 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