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 analysis, machine learning techniques, thermographic analysis, and SKF Machine Condition Advisor tools that demonstrates significant potential for predictive maintenance and intelligent condition monitoring applications.
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 framework integrating the minimum redundancy maximum relevance (mRMR) criterion with copula entropy (mRMR-CE). This framework utilizes 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 environments and with various classifiers, the method shows strong performance and stability. To validate the effectiveness of the proposed frame, seven feature selection approaches—CE, mRMR, mRMR-CE, Pearson, Principal Component Analysis, Hibert-Schmidt independence criterion—Lasso, and concrete autoencoder —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, Random Forest, multi-layer perceptron, and extreme gradient boosting. Experimental results demonstrate that the proposed method exhibits outstanding performance on both the Case Western Reserve University (CWRU) bearing fault dataset and the Unit 3 dataset from a hydropower plant. On the CWRU dataset, it achieved an average precision of 99.76% and an average F1 score of 96.57%, while on the Unit 3 dataset, it attained a 98.96% average accuracy and an average F1 score of 98.97%. 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 optimized through Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm, 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
Findings underscore the potential of higher-order statistical descriptors, particularly kurtosis, for EEG-based epileptic seizure detection under a controlled benchmark setting and underscore the potential of classical machine learning classifiers to distinguish normal and epileptic EEG signals.
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