Search Results (295)

Safety on construction sites is an essential yet challenging issue due to the inherently hazardous nature of these sites. Workers are expected to wear Personal Protective Equipment (PPE), such as helmets, vests, and safety glasses, to prevent or minimize their exposure to injuries. However, ensuring compliance remains difficult, particularly in large or complex sites, which require a time-consuming and usually error-prone manual inspection process. The research proposes an automated PPE detection system utilizing the deep learning model YOLO11, which is trained on the CHVG dataset, to identify in real-time whether workers are adequately equipped with the necessary gear. The proposed PPE-EYE method, using YOLO11x, achieved a mAP50 of 96.9% and an inference time of 7.3 ms, which is sufficient for real-time PPE detection systems, in contrast to previous approaches involving the same dataset, which required 170 ms. The model achieved these results by employing data augmentation and fine-tuning. The proposed solution provides continuous monitoring with reduced human oversight and ensures timely alerts if non-compliance is detected, allowing the site manager to act promptly. It further enhances the effectiveness and reliability of safety inspections, overall site safety, and reduces accidents, ensuring consistency in follow-through of safety procedures to create a safer and more productive working environment for all involved in construction activities. Full article

Rotating machinery plays a critical role in industrial systems, and effective anomaly detection and assessment are indispensable for ensuring operational safety and reliability. However, the performance of existing methods is often constrained by the difficulty in acquiring fault samples—such samples are typically scarce during the initial operational phase of equipment. To address this challenge, this paper proposes a novel anomaly detection and evaluation framework based on Data-Accumulation-Aware Generative Adversarial Networks (DAA-GANs) and similarity estimation. The core innovation of this framework lies in its adaptability across different data accumulation stages. During the early operational phase dominated by normal samples, only normal data is used to train the DAA-GAN to establish a baseline detector. As fault data gradually accumulates, the detection threshold undergoes adaptive adjustment through collaborative optimization of normal and abnormal samples, thereby enhancing the detector’s generalization capability. Upon amassing annotated fault samples of varying severity, the framework assesses anomaly severity by analyzing the similarity between test outputs of unknown samples and known fault samples. The framework is validated through two case studies: a fault simulation model for a torque-splitting transmission system and the publicly available Case Western Reserve University (CWRU) bearing dataset. In the simulation case, the detection accuracy reaches 100% for the gear tooth breakage levels. On the CWRU dataset, the proposed method achieves an overall average detection accuracy of 99.83% across three operating speeds (1730/1750/1772 rpm), and the similarity-based assessment provides consistent severity identification. These results demonstrate that the proposed framework can support reliable anomaly detection and severity assessments under progressive data accumulation. Full article

Drivetrain systems operate under varying operating conditions (OCs), which often obscure early-stage fault signatures and hinder robust condition monitoring (CM). This work introduces an AI digital platform developed during the EEDRIVEN project, featuring a holistic CM framework that integrates statistical time series methods—using Generalized AutoRegressive (GAR) models in a multiple model fault diagnosis scheme—with deep learning approaches, including autoencoders and convolutional neural networks, enhanced through a dedicated decision fusion methodology. The platform addresses all key CM tasks, including fault detection, fault type identification, fault severity characterization, and remaining useful life (RUL) estimation, which is performed using a dynamics-informed health indicator derived from GAR parameters and a simple linear Wiener process model. Training for the platform relies on a limited set of experimental vibration signals from the physical drivetrain, augmented with high-fidelity multibody dynamics simulations and surrogate-model realizations to ensure coverage of the full space of OCs and fault scenarios. Its performance is validated on hundreds of inspection experiments using confusion matrices, ROC curves, and metric-based plots, while the decision fusion scheme significantly strengthens diagnostic reliability across the CM stages. The results demonstrate near-perfect fault detection (99.8%), 97.8% accuracy in fault type identification, and over 96% in severity characterization. Moreover, the method yields reliable early-stage RUL estimates for the outer gear of the drivetrain, with normalized errors < 20% and consistently narrow confidence bounds, which confirms the platform’s robustness and practicality for real-world drivetrain systems monitoring. Full article

A machine learning (ML) methodology for the robust detection and severity characterization of incipient gear faults under variable speed and load is postulated. The methodology is trained using vibration signals from a single accelerometer mounted on the gearbox, simultaneously acquired with tachometer signals at a sample of working conditions (WCs) from the range of interest. A special parametric identification procedure of gearbox dynamics that may account for the continuous range of WCs is introduced through ‘clouds’ of advanced stochastic data-driven Functionally Pooled models, estimated from angularly resampled vibration signals. Each cloud represents the gearbox dynamics at a specific fault severity level, while the pseudo-static effects of the WCs on the dynamics are accounted for through data pooling. Fault detection and severity characterization are achieved by testing the consistency of a vibration signal with each model cloud within a hypothesis testing framework in which the unknown load is also estimated. The methodology is assessed through 18,300 experiments on a single-stage spur gearbox including four incipient single-tooth pinion faults, 61 speeds, and four load levels. The faults produce no significant changes in the time-domain signals, while their frequency-domain effects overlap with the variations caused by the WCs, rendering the diagnosis problem highly challenging. The comparison with a state-of-the-art deep Stacked Autoencoder (SAE) demonstrates the ML method’s superior performance, achieving 95.4% and 91.6% accuracy in fault detection and characterization, respectively. Full article

Background: The need to improve gait emulation in people with amputation has driven the development of customized prosthetic mechanisms. This study focuses on the design and validation of a mechanism for external knee joint prostheses, based on the trajectory of the Instantaneous Center of Rotation (ICR) of a healthy knee. Objective: The objective is to design a mechanism that accurately reproduces the evolution of the ICR trajectory, thereby improving stability and reducing the user’s muscular effort. Methods: An exploratory methodology was employed, utilizing computer-aided design (CAD), kinematic simulations, and rapid prototyping through 3D printing. Multiple configurations of four- and six-bar mechanisms were evaluated to determine the ICR trajectory and compare it with a reference model obtained in the laboratory from a specific subject, using MATLAB-2023a and the Fréchet distance as an error metric. Results: The results indicated that the four-bar mechanism, with the incorporation of a simple gear train, achieved a more accurate emulation of the ICR trajectory, reaching a minimum error of 6.87 mm. Functional tests confirmed the effectiveness of the design in terms of stability and voluntary control during gait. It can be concluded that integrating the mechanism with the gear train significantly enhances its functionality, making it a viable alternative for the development of external knee prostheses for people with transfemoral amputation, based on the ICR of the contralateral leg. Full article

Traditional fault diagnosis methods, which rely on single-vibration signals, are insufficient for capturing the complexity of mechanical systems. As neural networks evolve, attention mechanisms often fail to preserve local features, which can reduce diagnostic accuracy. Additionally, transfer learning using single-domain metrics struggles under fluctuating conditions. To address these challenges, this paper proposes an innovative adversarial training approach based on the Time–Frequency Fused Vision Transformer Network (TFFViTN). This method processes signals in both the time and frequency domains and incorporates a robust attention mechanism, along with a novel metric that combines Wasserstein distance and maximum mean discrepancy (MMD) to precisely align feature distributions. Adversarial training further strengthens domain-invariant feature extraction. Experiments on bearing and gear datasets demonstrate that our model significantly improves diagnostic performance, stability, and generalization. Full article

This study investigates Leonardo da Vinci’s long-standing interest in milling technologies through the digital reconstruction of a pendulum-driven mill illustrated in the Codex Atlanticus. By tracing the chronological development of Leonardo’s ideas across multiple sheets, this research highlights the continuity and evolution of his conceptual approach to energy transmission and mechanical automation. This work adopts a systematic design methodology to interpret and visualize the structural logic of the machine, integrating historical sources with engineering reasoning. The resulting CAD model reconstructs the key components (such as the gear train, escapement system, and pendulum) within a coherent architectural framework inspired by Leonardo’s sketches. While the digital model remains a preliminary interpretation, it offers a historically grounded basis for future refinements. In particular, it lays the groundwork for potential physical reconstructions intended for museum display and public engagement. This study contributes to the broader understanding of Renaissance mechanical culture and the role of digital tools in heritage preservation and dissemination. Full article

The present study proposes an integrated real-time energy management strategy (EMS) that combines an adaptive neuro-fuzzy inference system (ANFIS) with dynamic programming (DP) to enhance the energy efficiency and system durability of fuel cell electric commercial vehicles (FCECVs). Firstly, a comprehensive DP framework was established to optimize the EMS offline, which simultaneously considers power allocation and automated manual transmission (AMT) gear-shifting to minimize hydrogen consumption (HC). Then, the DP framework was employed to determine optimal power allocation patterns of the FCECVs under various initial state-of-charge (SOC) battery conditions. Based on the DP results, a novel real-time EMS integrating ANFIS with DP solution was developed to formulate an efficient fuzzy inference system (FIS), where the ANFIS model was trained using the particle swarm optimization (PSO) algorithm. The proposed ANFIS-DP EMS was evaluated through extensive simulations under stochastic driving cycles, with performance comparisons against both the DP method and conventional charge-depleting and charge-sustaining (CD-CS) strategies. The experimental results demonstrate that the ANFIS-DP maintains efficient FCS operation across diverse driving conditions while effectively controlling the rate of power change within optimal ranges. Compared to the CD-CS strategy, the proposed method achieves a substantial 14.98% reduction in HC, approaching the performance of DP (only 5.40% higher). Most notably, the ANFIS-DP strategy demonstrates remarkable computational efficiency improvements, outperforming DP by 96.13% and CD-CS by 22.05%. These findings collectively validate the effectiveness of our proposed approach in achieving real-time energy management optimization for FCECVs. Full article

The planetary bearing looseness fault can cause the planetary gear train to fail. Conventional modeling methods do not consider complex component-coupling relationships for fault feature analysis. As a result, a joint model is developed to examine the dominant relationship between planetary bearings and the planetary gear train. Firstly, the planetary bearing is modeled in the normal and fault states. Then, a refined joint planetary gear train dynamic model is constructed, which is composed of the planetary gears, the ring gear, the carrier, the sun gear, and the planetary bearings. Finally, the simulation results show that, when the planetary bearing is in the looseness fault state, its fault characteristic presents as the rotation frequency of the carrier and its harmonics. The on-site signal of a 2.0 MW wind turbine is used to verify the effectiveness of the model. The proposed model can provide the basis for the fault mechanism analysis and fault diagnosis of rolling bearing outer ring looseness. Full article

Reducing noise in electric multiple-unit (EMU) gearboxes demands prediction tools that are both rapid and reliable. Gear sound pressure levels vary sharply with micrometre-scale changes such as tooth repair, inclination, or profile relief, yet traditional estimates depend on hours-long CAE simulations. We present a data-driven hybrid surrogate that combines k-means clustering and inverse distance weighting (CLS-IDW) within the ODYSSEE A-Eye platform to map geometry modifications directly to broadband noise. Trained on the open 200-case Romax dataset, the model returns predictions within milliseconds and reproduces unseen operating points, with R² = 0.75 and a mean absolute error of 2.33 dB, matching solver repeatability. Sensitivity analysis identifies a −7° tooth inclination coupled with a 10 µm repair depth as the most effective combination, lowering noise by 3–5 dB. Eliminating costly CAE loops, the surrogate supports acoustics-aware optimisation at the concept stage, compressing development cycles and enhancing passenger comfort while maintaining transparency for regulatory review. Full article

Planetary gear trains offer numerous advantages over traditional gear systems, including high efficiency, the ability to handle large torque loads, and significant reductions in mass and size for the same torque capacity. However, their relatively complex design necessitates the use of optimization techniques to identify the most suitable configurations for specific applications. A key requirement for effective optimization is a mathematical model that accurately captures the essential operational characteristics of the system. Moreover, the optimization process must account for multiple, often conflicting, objectives. This paper focuses on the multicriteria optimization of a three-stage planetary gear train intended for use in a road vehicle winch. The development of the optimization model involves defining the objective functions, decision variables, and constraints. Optimization criteria were based on the following characteristics: overall volume, mass, transmission efficiency, and the production costs of the gear pairs. In addition to identifying the group of solutions that are Pareto optimal, the model employs the weighted coefficient method to select a single optimal solution from this set. The selected solution is then analyzed through simulation to assess potential gear failure scenarios. By combining optimization techniques with simulation and contact analysis, this study contributes to improving the reliability of planetary gear transmissions. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

The gear transmission system is a safety-critical component in rail transit, typically designed for a service life exceeding 20 years. Failure analysis of such systems remains a key focus for railway engineers. This study systematically investigates four representative cases of premature gear failure in high-speed trains using a standardized analytical procedure that includes visual inspection, chemical analysis, metallographic examination, scanning electron microscopy, and hardness testing. The results identify four primary root causes: subsurface slag inclusions in raw materials, inadequate heat treatment leading to a non-martensitic layer (∼60 μm) at the tooth root, grinding-induced temper burns (crescent-shaped "black spots") accompanied by a hardness drop of ∼100–150 HV, and insufficient lubrication. The interdependencies between these factors and failure mechanisms, e.g., fatigue cracking, spalling, and thermal scuffing, are analyzed. This work provides an evidence-based framework for improving gear reliability and proposes targeted countermeasures, such as ultrasonic inclusion screening and real-time grinding temperature control, to extend operational lifespans. Full article

The gearbox is essential for power transmission in high-speed trains, and its reliability directly impacts operational safety. Accurate monitoring data and effective assessment methods are crucial for accurately assessing its reliability. This study is based on digital twin (DT) technology, precisely deploying virtual sensors to collect vibration data from critical measurement points accurately. By integrating the Wild Horse Optimizer (WHO) and the Weibull Proportional Hazards Model (WPHM), it achieved reliability assessment for a high-speed train gearbox. First, a DT framework for the high-speed train gearbox was established. Taking the gear pair, a critical power transmission component in the gearbox, as an example, a DT model of the gear pair was built on Ansys Twin Builder, virtual sensors were deployed at critical measurement points, and vibration acceleration data was collected. Then, a WPHM reliability assessment model was established, and the WHO was used to estimate and optimize the WPHM parameters. Finally, the response covariates reduced by the Local Tangent Space Alignment (LTSA) were used as model inputs, and the WPHM was applied to assess the reliability of critical parts based on the collected data. The web-deployed DT model was delivered within 13 s. This achieved a simulation acceleration factor of 2.35 × 10⁴, compared to traditional methods. The number of iterations for the WOA was reduced by 62.9% compared to the WHO and by 48.1% compared to the HHO. The reliability assessment results align with the actual operating mileage status of the gear pair, thus validating the effectiveness and feasibility of this method. Full article

Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main landing gear strut of a Cessna 172. High-fidelity finite-element strain–life simulations were combined with a quadratic Ridge surrogate and a two-layer bootstrap to generate full probabilistic RUL distributions. The surrogate mapped five mass-and-balance inputs (fuel, front seats, rear seats, forward and aft baggage) to per-flight fatigue damage with high accuracy (R² = 0.991 ± 0.013). At the same time, ±3% epistemic confidence bands were attached via resampling. Borgonovo’s moment-independent Δ indices were applied to incremental damage (ΔD) in this context, revealing front-seat mass as the dominant driver of fatigue variability (Δ = 0.502), followed by fuel (0.212), rear seats (0.199), forward baggage (0.141), and aft baggage (0.100). The resulting RUL distribution spanned 9 × 10⁴ to >2 × 10⁶ cycles, with a fleet average of 0.41 million cycles (95% CI: 0.300–0.530 million). These results demonstrate that operational levers—crew assignment, fuel loading, and baggage placement—can significantly extend strut life. Although demonstrated on a specific training fleet dataset, the methodological framework is, in principle, transferable to other aircraft or mission types. However, this would require developing a new, component-specific finite element model and retraining the surrogate using a representative set of mass and balance records from the target fleet. Full article