Eng

The reliability and efficiency of induction motors in Industry 4.0 environments critically depend on advanced fault detection systems capable of real-time monitoring and diagnosis. This paper presents a novel deep learning approach combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for automated detection and classification of inter-turn short-circuit faults in three-phase induction motors. Our methodology processes three-phase current signals through a sophisticated CNN-LSTM architecture that extracts both spatial and temporal fault patterns. The proposed system classifies seven distinct motor conditions: healthy operation, three levels of high-impedance faults (HI-1 to HI-3), and three levels of low-impedance faults (LI-1 to LI-3). Experimental validation demonstrates exceptional performance, with the CNN-LSTM model achieving 97.2% accuracy, significantly outperforming traditional machine learning approaches, including SVM (66.3%), Random Forest (67.4%), and KNN (78.1%). The system provides real-time fault classification with inference times under 3 ms, making it suitable for continuous monitoring in smart manufacturing environments.

This study examines a top-chord-free open-web steel-truss composite floor in which the concrete slab functionally replaces the traditional top chord and works jointly with vertical square-tube web members and a square-tube bottom chord. Two scaled specimens—with and without concrete infill in the end shear-bending blocks—were fabricated and tested under static loading. The load–deflection response delineates three stages: elastic, elastic–plastic, and failure. Tests show that infilling the shear-bending blocks does not enhance global mechanical performance. In the elastic range, the mid-span open-web section satisfies the plane-section assumption with a linear strain profile, whereas the solid-web section exhibits a bilinear distribution. A validated ANSYS finite-element model reproduces the measured responses and supports a parametric study showing that span-to-depth ratio, opening-to-span ratio, slab (flange) thickness, and width-to-span ratio significantly affect ultimate capacity and deflection. Design recommendations are proposed: span-to-depth ratios of 11–14 and opening-to-span ratios of 0.04–0.07. An equivalent-stiffness-based simplified linear-elastic deflection formula with a reduction factor is derived, which accurately tracks deflection evolution and enables serviceability-driven selection of web spacing and overall structural depth.

Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module.