Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 3040-3045· 0 citations· 28 references
Abstract
This paper investigates the diagnosis of Inter-Turn Short-Circuit (ITSC) faults in Permanent Magnet Synchronous Machines (PMSM) under controlled experimental conditions. The study is based on stator current signals acquired for different speeds, load levels, and fault severities. Since time-domain waveforms under variable speed drive do not clearly reveal the fault, Shannon entropy is used to capture variations in signal complexity and extract a discriminative feature. This feature is then used as input to a Multi-Layer Perceptron (MLP) classifier. The experimental results show that the proposed approach can effectively distinguish healthy and faulty conditions, achieving mean values of 92.8% accuracy, 94% precision, 96.6% recall, and 95.2% F1-score. These results demonstrate the potential of Shannon entropy combined with MLP for reliable ITSC fault diagnosis in PMSMs.
This research presents an integrated condition monitoring framework for deep groove ball bearings by combining Complex Morlet Wavelet (CMW) analysis, machine learning techniques, thermographic analysis, and SKF Machine Condition Advisor tools. The proposed methodology employs Fast Fourier Transform (FFT) and Complex Morlet Wavelet-based vibration signal processing to extract discriminative time–frequency features for the early detection and diagnosis of bearing faults under both single and combined fault conditions. To evaluate fault classification performance, three machine learning algorithms, namely Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), are implemented and comparatively analyzed. Experimental investigations are conducted on a laboratory-scale bearing test rig operating under controlled conditions. The extracted wavelet-based features effectively characterize fault-induced vibration signatures, enabling accurate fault identification and classification. Comparative results indicate that Random Forest achieves the highest classification accuracy, followed by SVM and ANN. The average classification accuracies obtained using RF, SVM, and ANN are 97.48%, 95.27%, and 87.20%, respectively, demonstrating the superior robustness and generalization capability of the ensemble learning approach. Furthermore, thermographic analysis and SKF Machine Condition Advisor measurements provide complementary information for validating fault severity and machine health conditions, thereby enhancing diagnostic reliability. Although the present study is limited to constant-speed operation and controlled laboratory environments, the proposed framework demonstrates significant potential for predictive maintenance and intelligent condition monitoring applications. The integration of advanced time–frequency analysis, machine learning-based fault classification, and practical condition monitoring tools offers an effective and reliable solution for machinery health assessment in industrial environments.
M. Maurya, Chandrabhanu Malla, I. Panigrahi et al.· F1000Research· 0 citations
To address the challenges of conventional feature extraction methods in capturing nonlinear dependencies within fault signals and reducing feature redundancy during hydropower units fault diagnosis, this paper proposes a feature selection method integrating the Minimum Redundancy Maximum Relevance (mRMR) criterion with Copula Entropy (mRMR-CE). This method utilizes Copula Entropy (CE) to capture both linear and nonlinear dependencies in vibration signals. Combined with the mRMR criterion to suppress feature redundancy, it achieves stable selection of highly discriminative features. In noisy environment and with various classifiers, the method shows strong performance and stability. To validate the effectiveness of the proposed method, five feature selection approaches-CE, mRMR, mRMR-CE, Pearson, and PCA-were applied to the training samples during the feature selection stage. Lastly, the chosen features were input into four different types of classifiers for training and testing: Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). Experimental results demonstrate that the proposed method exhibits outstanding performance on both the CWRU bearing fault dataset and the Unit 3 dataset from a hydropower plant. It achieved an average accuracy of 99.87% and an F1 score of 98.25% on the CWRU dataset, and reached 100% accuracy with an F1 score of 99.31% on the Unit 3 dataset. These results significantly outperform traditional feature selection methods while demonstrating high stability and robustness.
Bo Li, Jiahao Li, Guangtao Zhang et al.· Engineering Research Express· 0 citations
This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks (ANNs) optimized through Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Vibration signals were collected from bearings operating under five health conditions (healthy, outer ring fault, inner ring fault, ball fault, and combined faults), at three rotational speeds, and along three measurement directions. The acquired signals were preprocessed using filtering, normalization, and segmentation. Time-domain and Fast Fourier Transform (FFT)-based frequency-domain features were extracted and used to train ANN models. The ANN architectures, including hidden layers, neurons, and activation functions, were optimized using BO, PSO, and GA, resulting in six configurations. Since the problem is a multi-class classification task, performance was assessed using F1-score, Accuracy, precision, and recall. The optimized ANN models were also benchmarked against Support Vector Machine (SVM), K-Nearest Neighbors (kNN), and Random Forest (RF) classifiers using the same feature sets. Results show that FFT -based features consistently outperformed time-domain features, and ANN-PSO with FFT-based features achieved the best performance, with F1-score = 0.982, Accuracy = 0.998, precision = 0.982, and recall = 0.982. This work contributes a systematic applied comparison of ANN optimization strategies rather than a fundamentally new machine-learning architecture, highlighting optimized ANNs as competitive and computationally efficient solutions for bearing fault diagnosis.
Khoualdia Kaaïs, Khoualdia Tarek, M. Nahal· International Journal of Pro...· 0 citations
To address the issues of insufficient feature extraction and low localization accuracy in distribution network fault diagnosis, this study proposes a fault classification and localization method based on APC-SVM and PC-AZOA. The model performs a simultaneous decomposition of three-phase signals using multivariate variational modal decomposition and employs the energy entropy of each model component as the feature vector; During the classification stage, the method integrates electrical and physical constraints, introducing three-phase energy imbalance and variance into the support vector machine ’ s parameter optimization process for the first time to dynamically adjust the penalty factor and kernel parameters; finally, a traveling wave propagation time error model is constructed, and an adaptive zebra optimization algorithm constrained by physical information is proposed. By innovatively embedding prior physical knowledge into the search space constraints, the method effectively suppresses invalid searches and improves convergence efficiency. Experimental results show that the model achieves a classification accuracy of up to 98.4% with a positioning error below 1%, demonstrating both high precision and high efficiency.
Dahua Li, Xinrui Yang, Yu Song et al.· 2026 IEEE International Conf...· 0 citations
Epileptic seizure detection from EEG signals remains challenging due to their non-stationary and complex nature. This study presents a comparative analysis of Discrete Wavelet Transform (DWT)-based feature extraction combined with classical machine learning classifiers (SVM, KNN, and MLP) to distinguish normal and epileptic EEG signals. Using the publicly available Bonn University dataset (Sets A and E), EEG signals were decomposed using the Daubechies-4 (db4) wavelet into five decomposition levels corresponding to standard frequency bands (Delta, Theta, Alpha, Beta, Gamma). Seven statistical features—energy, mean amplitude, standard deviation, Shannon entropy, relative wavelet energy (RWE), kurtosis, and skewness—were extracted from each sub-band. A stratified 10-fold cross-validation with a leakage-controlled record-level partitioning strategy was implemented to reduce optimistic bias. Since subject-level identifiers are unavailable in the public Bonn dataset, the validation was designed to avoid re-splitting individual EEG records across training and testing stages. Results demonstrate that kurtosis-based features consistently achieve the highest accuracy (99.8% ± 0.3) across all classifiers, significantly outperforming other features (p < 0.01). These findings underscore the potential of higher-order statistical descriptors, particularly kurtosis, for EEG-based epileptic seizure detection under a controlled benchmark setting.
H. Hindarto, Ade Eviyanti, A. Ahfas et al.· International journal of ele...· 0 citations
This paper presents a comparative study for epilepsy monitoring using EEG signals along two main axes. The first axis consists of comparing the performance of the differentiation technique, which is known to be very important for the study of non-stationarity, with wavelet transform, which is widely used for detecting different brain rhythms. The second part aims to compare the performance of different machine learning algorithms, including k-Nearest Neighbors (k-NN), Decision Trees, Random Forest, and Support Vector Machines (SVM). Used features are statistical and higher order statistics characteristics such as mean, standard deviation, median, Min-Max, Kurtosis and Skewness. We tested our approach on publicly available and widely used datasets in the literature, namely the University of Bonn dataset and the Bern-Barcelona dataset. The experimental results demonstrate the effectiveness of the differentiation method as an important tool for EEG preprocessing, leading to very high performance.
Ines Bouzouita, Zayneb Brari, S. Belghith· International Conference on...· 0 citations