Researchers have introduced a layer-by-layer discriminative transfer learning method that enhances cross-domain diagnostic accuracy for rotating machinery bearing faults under ...
Comparative laboratory testing reveals that mechanical vibration signals combined with wrapper-based machine learning provide superior diagnostic robustness over electrical current ...
A new artificial intelligence framework improves connected vehicle predictive maintenance by fusing internal diagnostic telemetry with external environmental and behavioural ...
A physics-informed graph neural network achieved 92.48% overall accuracy in predicting industrial robotic arm fatigue life across variable-load conditions.
Continuous AI-driven monitoring in Leipzig and Bergamo detected hidden broken rails and overhead line faults during regular service, while a ...
A systematic review of 355 documents shows research on human factors in industrial maintenance growing at 11.69 percent annually, led ...
A new framework combines 3D computational fluid dynamics, sparse Proper Generalized Decomposition surrogates, and data-driven methods such as SINDy and ...
Researchers report a hybrid BiLSTM framework with wavelet feature extraction and modified Archimedes optimization that classifies composite insulator aging states ...
A newly published study in Discover Artificial Intelligence proposes a machine learning framework designed specifically for this problem, combining a ...
A systematic review of 75 studies finds generative AI advancing renewable energy globally, but almost no African field validation exists ...
Researchers have built an explainable stacking machine learning framework that jointly forecasts electrical load and predicts transformer remaining lifespan in ...
A hydropower-oriented AI framework combining physics-based residuals, constrained synthetic data generation, and deep residual networks achieves 98.6 percent accuracy on ...
Researchers combined a low-cost IoT monitoring prototype with a Bayesian-optimized XGBoost model that achieved the highest accuracy and fastest computation ...
A hybrid framework that turns voltage and current measurements into resistance-based statistical features enabled Random Forest to classify electrical faults ...
A label-free Incremental PCA method produced a smooth internal-reliability signal across industrial and wireless sensor streams, but its response to ...
Researchers in Türkiye have combined IoT sensors, gradient-boosted machine learning, and a safely grounded large language model into a prototype ...
A benchmark study found that an AI service hub combining sensor, text and visual data achieved high fault-detection and maintenance ...
Researchers at TU Braunschweig have built a transparent, human-in-the-loop decision-support system that fuses maintenance, inventory, bottleneck, and passenger-assistance signals into ...
A comprehensive review of 120 studies finds that hybrid AI and machine learning architectures are transforming electric vehicle battery management, ...
A new YOLO-based framework called GSDD-Net detects subtle gear surface defects in cluttered industrial images with higher accuracy, a smaller ...
