Search Results (233)

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

In response to the growing demand for lightweight and high-efficiency industrial equipment, this study addresses the critical issue of lubrication failure in high-speed, heavy-duty gear reducers, which often leads to reduced transmission efficiency and premature mechanical damage. A three-dimensional transient multiphysics-coupled model of oil-jet lubrication is developed based on computational fluid dynamics (CFD). The model integrates the Volume of Fluid (VOF) multiphase flow method with the shear stress transport (SST) k−ω turbulence model. This framework enables the accurate capture of oil-jet interface fragmentation, reattachment, and turbulence-coupled behavior within the gear meshing region. A parametric study is conducted on oil injection velocities ranging from 20 to 50 m/s to elucidate the coupling mechanisms between geometric configuration and flow dynamics, as well as their impacts on oil film evolution, energy dissipation, and thermal management. The results reveal that the proposed method can reveal the dynamic evolution characteristics of the gear injection lubrication. Adopting an appropriately moderate injection velocity (30 m/s) improves oil film coverage and continuity, with the lubricant transitioning from discrete droplets to a dense wedge-shaped film within the meshing zone. Optimal lubrication performance is achieved at this velocity, where oil shear-carrying capacity and kinetic energy utilization efficiency are maximized, while excessive turbulent kinetic energy dissipation is effectively suppressed. Dynamic monitoring data at point P further corroborate that a well-tuned injection velocity stabilizes lubricant-velocity fluctuations and improves lubricant oil distribution, thereby promoting consistent oil film formation and more efficient heat transfer. The proposed closed-loop collaborative framework—comprising model initialization, numerical solution, and post-processing—together with the introduced quantitative evaluation metrics, provides a solid theoretical foundation and engineering reference for structural optimization, energy control, and thermal reliability design of gearbox lubrication systems. This work offers important insights into precision lubrication of high-speed transmissions and contributes to the sustainable, green development of industrial machinery. Full article

Accurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical safety applications. To address the dual challenges of prediction accuracy and model interpretability, a multi-agent reinforced symbolic regression (MA-RSR) framework is proposed by integrating multi-agent reinforcement learning with symbolic regression (SR) techniques. Specifically, MA-RSR employs a collaborative mechanism that decomposes complex mathematical expressions into parallel components constructed by independent agents, effectively addressing the search space explosion problem in traditional SR. The system incorporates Transformer-based architecture to enhance symbolic selection capabilities, while an intelligent masking mechanism ensures mathematical rationality through multi-level constraints. To demonstrate effectiveness of the proposed method, validation is conducted using SAE4340 steel multiaxial fatigue data and landing gear finite element simulation. The MA-RSR framework successfully discovers two mathematical expressions achieving R² of 0.96. Compared to traditional empirical formulas, MA-RSR achieves prediction accuracy improvements exceeding 50% while providing complete interpretability that machine learning methods lack. Furthermore, the multi-agent collaborative mechanism significantly enhances search efficiency through parallel expression construction compared to existing symbolic regression approaches. Full article

This article analyzes statistics on the failure of technological equipment, assemblies, and mechanisms of agricultural (and other) machines associated with the breakdown or failure of gear pumps. It was found that the leading causes of gear pump failures are the opening of gear teeth contact during pump operation, poor assembly, wear of bushings, thrust washers, and gear teeth. It has also been found that there is a problem related to the restoration, repair, and manufacture of parts in the conditions of enterprises serving the agro-industrial complex of the Republic of Kazakhstan (AIC RK). This is due to the lack of necessary technological equipment, tools, and instruments, as well as centralized repair and restoration bases equipped with the required equipment. This work proposes to solve this problem by applying AM technologies to the repair and manufacture of parts for agricultural machinery and equipment. The study results on the stress–strain state of support bushings under various pressures are presented, showing that a fully filled bushing has the lowest stresses and strains. It was also found that bushings with 50% filling and fully filled bushings have similar stress and strain values under the same pressure. The difference between them is insignificant, especially when compared to bushings with lower filling. This means that filling the bushing by more than 50% does not provide a significant additional reduction in stresses. In terms of material and printing time savings, 50% filling may also be the optimal option. Full article

To address traditional methods’ limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock–degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel–Clayton–Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox’s planetary gear-stage system covers four scenarios: natural degradation, shock–degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model’s effectiveness and alignment with real-world operational conditions. Full article

Under the trend of lightweight and high-efficiency development in industrial equipment, precise regulation of lubrication in gear reducers is a key breakthrough for enhancing transmission system efficiency and reliability. This study establishes a three-dimensional numerical model for high-speed gear jet lubrication using computational fluid dynamics (CFD) and dynamic mesh technology. By implementing the volume of fluid (VOF) multiphase flow model and the standard k-ω turbulence model, the study simulates the dynamic distribution of lubricant in gear meshing zones and analyzes critical parameters such as the oil volume fraction, eddy viscosity, and turbulent kinetic energy. The results show that reducing the oil injection distance significantly enhances lubricant coverage and continuity: as the injection distance increases from 4.8 mm to 24 mm, the lubricant shifts from discrete droplets to a dense wedge-shaped film, mitigating lubrication failure risks from secondary atomization and energy loss. The optimized injection distance also improves the spatial stability of eddy viscosity and suppresses excessive dissipation of turbulent kinetic energy, enhancing both the shear-load capacity and thermal management. Dynamic data from monitoring point P show that reducing the injection distance stabilizes lubricant velocity and promotes more consistent oil film formation and heat transfer. Through multiphysics simulations and parametric analysis, this study elucidates the interaction between geometric parameters and hydrodynamic behaviors in jet lubrication systems. The findings provide quantitative evaluation methods for structural optimization and energy control in gear lubrication systems, offering theoretical insights for thermal management and reliability enhancement in high-speed transmission. These results contribute to the lightweight design and sustainable development of industrial equipment. Full article

Gearboxes are critical mechanical components in various modern constructions, including wind turbines, making their real-time monitoring and the prevention of major failures essential. Machine learning (ML) offers a precise and robust method for early-stage failure detection and efficient gear monitoring during operation, with computational efficiency that allows for use on edge devices. This article presents a method for detecting surface damage on gear teeth using unsupervised machine learning. Using only experimentally measured vibrational signals from a healthy gearbox as a training set, novel neural network architectures, including convolutional and recurrent autoencoders, were employed and compared with a classical dense autoencoder. The study confirmed the effectiveness of these methods in gear transmission diagnostics and demonstrated the potential for achieving high-quality classification metrics using unsupervised learning. Full article

With engineering plastics increasingly replacing traditional materials in various drive and control gear systems across numerous industrial sectors, material selection for any gearwheel critically impacts its mechanical and thermal properties. This paper investigates the engagement of steel and Polyvinylidene Fluoride (PVDF) gear pairs tested under several load conditions to determine polymer gears’ characteristic service life and failure modes. Furthermore, recognizing that the application of polymer gears is limited by insufficient data on their temperature-dependent mechanical properties, this study establishes a correlation between the tribological contact, meshing temperatures, and wear coefficients of PVDF gears. The results demonstrate that the flank surface wear of the PVDF gears is directly proportional to the temperature and load level of the tested gears. Several distinct load-induced failure modes have been detected and categorized into three groups: abrasive wear resulting from the hardness disparity between the engaging surfaces, thermal failure caused by heat accumulation at higher load levels, and tooth fracture occurring due to stiffness changes induced by the compromised tooth cross-section after numerous operating cycles at a specific wear rate. Full article

Spiral bevel gears are used in a wide range of industries, such as automotive and aerospace, to transfer power between intersecting axes. However, a certain level of vibration is always present in the systems, primarily due to the complex dynamic forces generated during the meshing of the gear teeth affected by the tooth profile. To address these challenges, this research developed a comprehensive dynamic model with eight degrees of freedom, capturing both translational and rotational movements of the system’s components. The study focused on evaluating the effects of two different tooth profile modifications, namely topology and flank modifications, on the vibration characteristics of the system. The system comprised a spiral bevel gear pair with mesh stiffness in forward rotation. The results highlighted that optimizing the tooth profile and minimizing tooth surface deviation significantly reduce vibration amplitudes and improve dynamic stability. These findings not only enhance the performance and lifespan of spiral bevel gears but also provide a robust foundation for the design and optimization of advanced gear systems in industrial applications, ensuring higher efficiency and reliability. In this paper, it was observed that some modifications led to a 68% reduction in vibration levels. Additionally, three modifications helped improve the vibrational behavior of the system, preventing chaotic behavior, which can lead to system failure, and transforming the system’s behavior into periodic motion. Full article

Aimed at dealing with the problems of high reliability solution cost and low solution accuracy under random excitation, especially Gaussian white noise excitation, this paper proposes a probability density evolution and reliability analysis method for nonlinear gear transmission systems under Gaussian white noise excitation based on the path integration method. This method constructs an efficient probability density evolution framework by combining the path integration method, the Chapman–Kolmogorov equation, and the Laplace asymptotic expansion method. Based on Rice’s theory and combined with the adaptive Gauss–Legendre integration method, the transient and cumulative reliability of the system are path integration method calculated. The research results show that in the periodic response state, Gaussian white noise leads to the diffusion of probability density and peak attenuation, and the system reliability presents a two-stage attenuation characteristic. In the chaotic response state, the intrinsic dynamic instability of the system dominates the evolution of the probability density, and the reliability decreases more sharply. Verified by Monte Carlo simulation, the method proposed in this paper significantly outperforms the traditional methods in both computational efficiency and accuracy. The research reveals the coupling effect of Gaussian white noise random excitation and nonlinear dynamics, clarifies the differences in failure mechanisms of gear systems in periodic and chaotic states, and provides a theoretical basis for the dynamic reliability design and life prediction of nonlinear gear transmission systems. Full article

Helical idler gear transmission systems can adapt to high-speed, heavy-load working environments and are thus widely used in aerospace, shipbuilding, and other heavy industry sectors. Root crack is one of the common fault types. Prior studies generally only considered cracks at a single working flank, lacking comparative analysis between the crack at the working flank and the non-working flank. This paper examines the dynamic response of helical idler gears with root cracks at different working flanks, comparing dynamic response differences between working and non-working flank cracks. First, a comprehensive dynamics model of the helical idler gear system is established. Second, the influence of root crack location (the working flank or the non-working flank) on time-varying meshing stiffness is considered based on potential energy method, and a flexible model is established by finite element method for the faulty gear. Finally, solution results of the rigid-flexible coupling dynamics model are analyzed. The dynamic response signal characteristics of root cracks at the working flank and the non-working flank are analyzed in time domain, frequency domain and time frequency domain, respectively. Corresponding experiments are designed based on the FZG experimental platform, and the experimental results are in good agreement with the simulation results, which verified the accuracy of the model. Full article

This paper addresses inherent limitations in unmanned aerial vehicle (UAV) undercarriages hindering vertical takeoff and landing (VTOL) capabilities on uneven slopes and obstacles. Robotic landing gear (RLG) designs have been proposed to address these limitations; however, existing designs are typically limited to ground slopes of 6–15°, beyond which rollover would happen. Moreover, articulated RLG concepts come with added complexity and weight penalties due to multiple drivetrain components. Previous research has highlighted that even a minor 3-degree slope change can increase the dynamic rollover risks by 40%. Therefore, the design optimization of robotic landing gear for enhanced VTOL capabilities requires a multidisciplinary framework that integrates static analysis, dynamic simulation, and control strategies for operations on complex terrain. This paper presents a novel, hybrid, compliant, belt-driven, three-legged RLG system, supported by a multidisciplinary design optimization (MDO) methodology, aimed at achieving enhanced VTOL capabilities on uneven surfaces and moving platforms like ship decks. The proposed system design utilizes compliant mechanisms featuring a series of three-flexure hinges (3SFH), to reduce the number of articulated drivetrain components and actuators. This results in a lower system weight, improved energy efficiency, and enhanced durability, compared to earlier fully actuated, articulated, four-legged, two-jointed designs. Additionally, the compliant belt-driven actuation mitigates issues such as backlash, wear, and high maintenance, while enabling smoother torque transfer and improved vibration damping relative to earlier three-legged cable-driven four-bar link RLG systems. The use of lightweight yet strong materials—aluminum and titanium—enables the legs to bend 19 and 26.57°, respectively, without failure. An animated simulation of full-contact landing tests, performed using a proportional-derivative (PD) controller and ship deck motion input, validate the performance of the design. Simulations are performed for a VTOL UAV, with two flexible legs made of aluminum, incorporating circular flexure hinges, and a passive third one positioned at the tail. The simulation results confirm stable landings with a 2 s settling time and only 2.29° of overshoot, well within the FAA-recommended maximum roll angle of 2.9°. Compared to the single-revolute (1R) model, the implementation of the optimal 3R Pseudo-Rigid-Body Model (PRBM) further improves accuracy by achieving a maximum tip deflection error of only 1.2%. It is anticipated that the proposed hybrid design would also offer improved durability and ease of maintenance, thereby enhancing functionality and safety in comparison with existing robotic landing gear systems. Full article

Gearboxes are autonomous devices essential for power transmission in various mechanical systems. When a failure occurs, it can lead to an inability to perform the required functions, potentially resulting in a complete shutdown of the mechanism and causing significant operational disruptions. Consequently, deploying expert methods for fault detection and diagnosis is crucial to ensuring the reliability and efficiency of these systems. Artificial intelligence (AI) techniques show promise for fault diagnosis, but their accuracy can be hindered by noise and manufacturing imperfections that distort mechanical signatures. Thorough data analysis and preprocessing are vital to preserving these critical features. Validating approaches through numerical simulations before experimentation is essential to identify model limitations and minimize risks. A hybrid approach, combining AI and physics-based models, could provide a robust solution by leveraging the strengths of both domains: AI for its ability to process large volumes of data and physics-based models for their reliability in modeling complex mechanical behaviors. This paper proposes a comprehensive diagnostic methodology. It starts with feature extraction from time-domain analysis, which helps identify critical indicators of gearbox performance. Following this, a feature selection process is applied using the Fisher criterion, which ensures that only the most relevant features are retained for further analysis. These selected features are then employed to train an Adaptive Neuro-Fuzzy Inference System (ANFIS), a sophisticated approach that combines the learning capabilities of neural networks with the reasoning abilities of fuzzy logic. The proposed methodology is evaluated using a dataset of gear faults generated through energy simulations based on a six-degree-of-freedom (6-DOF) model, followed by a secondary validation on an experimental dataset. Full article

Early failure detection in gear systems reduces unplanned downtime and associated maintenance costs in rotating machinery. Although numerous indicators can be extracted from vibration signals, selecting the most relevant ones remains challenging. This study proposes a methodology for selecting time-domain features to classify fault severity levels in spur gearboxes. Vibration signals are acquired using six accelerometers and processed to extract 64 statistical condition indicators (CIs). The most informative subset of CIs is identified and selected through a wrapper-based selection approach and artificial intelligence tools. The selected features are then evaluated based on the classification accuracy and the area under the curve (AUC) in receiver operating characteristic (ROC) achieved using Random Forest (RF) and K-nearest neighbours (K-NN) models, with performance exceeding 98%. Additionally, the effect of sensor position and inclination on signal quality and classification performance is analysed using factorial analysis of variance (ANOVA) and multiple comparison tests. The results confirm the robustness of the selected CIs and the minimal influence of sensor placement variability, supporting the practical applicability of the proposed approach in industrial settings. The methodology offers a structured framework for selecting condition indicators in vibration signals, experimentally validated using multiple sensors and fault severity levels, and it is both automated and straightforward to implement. Full article

Wind power plays a significant role in sustainable energy production, but the reliability of wind turbines depends heavily on the integrity of their gearboxes. Gearbox failures can lead to significant downtime and operational disruption. In this context, this paper provides an overview of the evolution of gearbox monitoring techniques, culminating in the emergence of digital twin (DT) technology. We explore the application of DT technology to gearbox condition monitoring, focusing on two critical components: bearings and gears. This includes a comprehensive review of methodologies involving model-based approaches and data-driven techniques using signal processing (SP) and artificial intelligence (AI) algorithms. We address the challenges of “learning with minimal knowledge” and propose a framework for the effective application of DT technology. Finally, we discuss future research directions and potential contributions to advancing the field of gearbox condition monitoring through the continued development and implementation of DT-based solutions. Full article