Electrical induction generators are vulnerable to winding faults that can degrade operational reliability and lead to unplanned maintenance. This study proposes a multiple-convolutional neural network (Multi-CNN) extreme ensemble learning framework for intelligent fault diagnosis and maintenance decision support using three-phase current and voltage measurements. Experimental recordings representing healthy operation, inter-turn faults, and inter-winding faults were segmented into non-overlapping 200-sample windows. Hjorth activity, mobility, and complexity were calculated for the three-phase current signals and the three-phase voltage signals, producing 18 features for each of 900 instances. Four convolutional neural network architectures were trained, and their class-probability outputs were combined through an extreme learning machine. Stratified blocked five-fold cross-validation was used to evaluate the models while preserving the chronological structure of the data. The proposed ensemble achieved 98.111% accuracy, 98.146% precision, 98.111% recall, 98.108% F1-score, and 97.167% Matthews correlation coefficient, correctly classifying 883 of 900 out-of-fold instances. It also attained a macro-averaged area under the receiver operating characteristic curve of 0.995. These results demonstrate that Hjorth-based electrical-signal characterization and Multi-CNN ensemble fusion can provide accurate and computationally efficient support for fault identification and predictive maintenance decisions in electrical induction generators.
M. Ahmed, Ahmed Mohammed Mohsin Alzubaidi, Z. Khan et al.· Applied System Innovation· 0 citations
Reliable in situ monitoring is essential for the improvement of process supervision, quality assurance, and machine-state recognition in additive manufacturing of smart composite systems. This study presents a non-invasive acoustic–vibration side-channel monitoring framework for identifying FDM printing cases using experimental recordings from two printers, Bambu Lab A1 mini and Bambu Lab P1P. Four representative printing cases were investigated: simple key, hard key, two keys, and retraction test. Raw acoustic and vibration signals were converted into interval-level statistical features, including six acoustic descriptors and 18 vibration descriptors extracted from the X, Y, and Z axes. A baseline deep neural network (DNN) and an enhanced residual attention deep neural network (RA-DNN) were implemented under acoustic-only, vibration-only, and fused acoustic–vibration input conditions using stratified five-fold cross-validation. The fused acoustic–vibration features achieved the best performance, with the RA-DNN reaching 96.75% accuracy, 96.91% precision, 96.75% recall, and 96.70% F1-score for the A1 mini, and 98.19% accuracy, 98.25% precision, 98.19% recall, and 98.18% F1-score for the P1P. These results indicate that acoustic–vibration side-channel signals can provide effective process signatures for intelligent and quality-aware FDM monitoring.
T. Mahmood, O. Abdullah, A. Hadi et al.· Journal of Composites Scienc...· 0 citations
This study aims to find the impact of lattice-core geometry on the tensile and flexural properties of FDM-printed PETG-sandwich panels. Six core profiles (namely square, circular, elliptical, octagonal, pentagonal and hexagonal) were fabricated under controlled parameters of the FDM and tested based on ASTM D638 and D790. The pentagonal core resulted in the highest flexural strength (23.2 MPa), peak load (322 N), and the hexagonal core in the highest tensile strength (24.8 MPa). The variation in tensile and flexural strength was highly explained by core geometry (93.4% and 93.8% respectively) (ANOVA, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.001$$\end{document}). The Taguchi S/N ratio evaluation demonstrated that both tensile and flexural performances exhibit comparable high sensitivity to core geometry variations (a relative difference of approximately 2.5%). A flexural strength was predicted by an elastic–plastic FEA model created in ABAQUS/CAE, with an error margin of 4.1% when compared to experimental results. These PETG panels experienced a strength loss of 12–16% but a 58–63% increase in ductility when compared to PLA + panels with the same core topology, demonstrating that core topology dictates performance across the evaluated thermoplastic matrices. The results will be useful for quantitatively suggesting the topology for FDM-fabricated sandwich structures.
A. Ogaili, Abdul-Rasool Kareem Jweri, S. Amin et al.· Journal of engineering and a...· 0 citations
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