Multi-scale wavelet feature fusion with transformer for fault diagnosis of 25 Hz phase-sensitive track circuit

Multi-Domain Feature Engineering for Noise-Tolerant Fault Classification in Analog Filter Circuits

This paper proposes a method of fault detection in analog circuits which involves various steps, including selection of benchmark circuits, dataset preparation, signal decomposition, model training, and performance analysis. The main aim of this work is to provide solid performance even in noisy environments. Monte Carlo analysis is used to generate a synthetic dataset with 200 runs per fault class by introducing component tolerances and realistic faults. A multi-stage pipeline is proposed; it begins with resampling the signals and normalizing them, and then noise is added at different levels: 5 dB, 10 dB and 20 dB. Feature fusion is performed by combining time-, frequency-, and statistical-domain features. Statistical-domain features are extracted by applying Variational Mode Decomposition (VMD) to split them into four IMF levels, followed by the application of Continuous Wavelet Transform (CWT) for time–frequency-domain analysis. Support Vector Machine (SVM), Random Forest, and Gradient Boosting are used as base-level classification models. A stacking ensemble model is developed which uses Random Forest, Gradient Boosting, and Extra Trees as base learners and Logistic Regression as the meta-learner.

Data-Driven Noise Suppression and Multiresolution Feature Fusion with Feature Space Refinement for Bearing Fault Diagnosis

Arc-Fault Detection Using Stage-Wise Alignment and Feature Fusion of Dual Learnable Time–Frequency Representations

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.

Adaptive Noise-Aware Bearing Fault Diagnosis via FFT Windowing and Wavelet-Based SNR-Guided LSTM Model Selection with Real-Time FPGA Implementation

This paper presents an adaptive noise-aware bearing fault diagnosis framework that integrates Fast Fourier Transform (FFT)-windowed feature extraction, wavelet-based Signal-to-Noise Ratio (SNR) estimation, and Long Short-Term Memory (LSTM) classification to maintain high diagnostic accuracy under both clean and severely noisy operating conditions. The core novelty of the proposed framework lies in its adaptive model selection mechanism, which automatically selects the most appropriate LSTM classifier according to the estimated SNR, thereby improving diagnostic robustness across different noise environments. Experiments were conducted on two benchmark datasets, the Case Western Reserve University (CWRU) dataset and the Huazhong University of Science and Technology (HUST) bearing dataset, to evaluate the generalization capability of the proposed approach. Two preprocessing pipelines were examined: time-domain normalization before FFT and frequency-domain normalization after FFT. Vibration signals were segmented without overlap to ensure unbiased evaluation. The results demonstrate that both the choice of window function and the normalization strategy significantly influence classification accuracy and robustness. Under noise-free conditions, several window types achieved accuracies above 99%, with triangular and Hamming windows providing the best performance. The combination of triangular windowing and time-domain normalization achieved the highest accuracy of 99.69%. Furthermore, time-domain normalization combined with triangular windowing exhibited superior stability and noise resistance compared with frequency-domain normalization. Under noisy conditions, noise-augmented training was found to be essential for achieving robust generalization. Models trained with moderate noise levels (8–12 dB) provided the best trade-off between accuracy and robustness, whereas excessive noise during training degraded performance. To accommodate varying noise environments, a lightweight wavelet-based SNR estimator was used to categorize operating conditions into low-, medium-, and high-SNR regions and select the corresponding LSTM classifier. The proposed framework was successfully implemented on a ZedBoard FPGA (Field-Programmable Gate Array) development board using a System-on-Chip (SoC) architecture. Experimental results show that, with a sampling frequency of 48 kHz and a processing window of 2048 samples, the proposed system updates the diagnostic result every 42.7 ms, demonstrating its suitability for real-time industrial condition monitoring and intelligent predictive maintenance applications.

Low-Level Inter-Turn Fault Detection Algorithm for Transformer Differential Protection

This paper presents a novel hybrid protection scheme based on Maximal Overlapped Discrete Wavelet Transform (MODWT) energy and a specialized difference function (DF) to accurately detect low-level inter-turn short-circuit faults in power transformers while maintaining high-selectivity features against transient conditions. Low-level inter-turn short-circuit faults (LIFs) in power transformers start at a low level and gradually spread to other windings. It is crucial to detect the fault in early stages and prevent further damage by disconnecting the faulty transformer immediately. A wavelet transform and difference function-based Transformer Differential Protection (TDP) algorithm is proposed in this paper. A differential protection scheme consists of two stages: feature extraction and fault detection. Maximum Overlapped Discrete Wavelet Transform (MODWT) energy and a difference function are used for feature extraction and an analytical logic is used for fault detection. It is also shown that this combination provides more reliable differential protection scheme than TDP with the wavelet transform only or TDP with a difference function (DF) alone. The method is assessed with experimental datasets collected from a laboratory-based, custom-built transformer which is specifically designed for validating the methods to detect LIFs. The method is evaluated according to a confusion matrix method with accuracy, dependability and sensitivity indices. The proposed TDP method detected all LIF cases, representing less than 2% of total windings. Therefore, the proposed hybrid algorithm represents an innovative step in applied system monitoring by providing a high-precision, software-based solution that enhances the operational reliability and resilience of existing TDP systems without requiring additional hardware.

A Vision Transformer for Bearing Fault Diagnosis Based on Wavelet Transform and Frequency-Domain Circulant Attention

Deep learning models often struggle with complex noise and variable loads in industrial fault diagnosis. This paper proposes a highly efficient framework, the Frequency-domain Circulant Attention Vision Transformer (FC-ViT), for robust rotating machinery monitoring. Raw vibration signals are first converted into 2D time-frequency representations via Continuous Wavelet Transform (CWT). FC-ViT then utilizes an improved Frequency-domain Circulant Attention mechanism, which achieves a log-linear computational complexity of $O(N \log N)$ , to isolate fault-related impulses from heavy stochastic and non-Gaussian impulsive noise. Validated on the Case Western Reserve University (CWRU) and Paderborn University (PU) datasets, FC-ViT achieves 100% accuracy under noise-free conditions. At an extreme −5 dB SNR, it maintains 89.4%–98.3% accuracy, significantly outperforming both state-of-the-art 1D diagnostic networks and generic 2D vision models (e.g., Swin-Transformer, ConvNeXt, and CA-DeiT). These results demonstrate superior noise immunity and cross-load generalization. Furthermore, comprehensive hardware deployment evaluations confirm its ultra-low inference latency and minimal memory footprint, providing a practical and real-time solution for real-world industrial condition monitoring.