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...
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 w...
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 bea...
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 varia...
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...
Ines Bouzouita, Zayneb Brari, S. Belghith· International Conference on...· 0 citations
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