Browse Topic: Reliability
Multi-UAV cooperative localization can utilize information fusion between nodes to improve localization accuracy and performance on the target. Distributed state fusion estimation methods have been heavily studied in recent years, but the final estimates in the research results do not converge towards the global optimum. This paper aims to make the state estimates of each individual in the UAV formation for the target converge and converge to reliable values. In this paper, we study a multi-UAV cooperative tracking method based on adaptive weighted fusion, which first evaluates the importance of each node in the UAV formation and the reliability of the local filtering estimation results, and then assigns the weights according to the reliability of the UAV’s local state estimation of the target in the whole at the current moment. Finally, this paper verifies through simulation experiments that the method can not only accomplish the state tracking of the target, but also that the state estimates of each node in the network converge to more accurate state estimates.
The gearbox is a key component of the mechanical transmission system, and its fault diagnosis is essential to the reliability of the equipment. However, obtaining fault samples under actual working conditions for gearbox fault diagnosis is challenging. In this paper, the rigid-flexible coupling dynamic simulation model of the gearbox is established, and the co-simulation of gear normal, crack, and breakage is carried out in the ADAMS and MATLAB environments. The comparison between the simulated and measured signals shows that the simulation method can accurately reflect the key characteristics, such as rotation frequency and meshing frequency, and verify its reliability and accuracy. The research results can provide effective data support for gearbox fault diagnosis and improve the operational safety of mechanical systems.
Aiming at the problem of insufficient modeling of spatio-temporal heterogeneity in road traffic accident prediction, a dual task machine learning framework integrating geographical environment, location attributes and time periodicity is proposed. The dataset used in this study was derived from traffic accident records of Nanchang during 2019–2023. Firstly, geographical identifiers are generated by rounding and aggregating latitude and longitude coordinates. At the same time, the location type is processed by a one-hot encoding, so as to carry out spatial clustering analysis of accident hotspots. Compared with the North-South pattern, the contribution of geographical features shows a strong East-West trend. The kernel density heatmap identified Zone A and zone B as dual core high-risk areas. Secondly, the sinusoidal/cosine function is used to encode the time feature circularly, which effectively captures the daily change of the accident. The quantitative analysis of random forest regression model showed that time characteristics accounted for 89.2% of the variance of accident frequency interpretation, significantly exceeding the contribution of geographical factors (10.2%) and location attributes (0.6%). After hyperparameter optimization, the accuracy of XGBoost classifier in predicting serious accidents is 75.97%, and the AUC value is 0.8412, which has strong robustness, and provides reliable support for dynamic risk assessment of traffic management system.
This SAE standard establishes the requirement for suppliers to plan a reliability program that satisfies the following three requirements: a The supplier shall ascertain customer requirements b The supplier shall meet customer requirements c The supplier shall assure that customer requirements have been met
This study investigates the use of the Overset mesh method for propeller simulations in OpenFOAM and compares it with the Arbitrary Mesh Interface (AMI) approach. While AMI is well validated for rotor aeroacoustics, it is limited in handling large relative motions and complex component interactions. In contrast, the Overset method enables flexible simulation of transition kinematics using overlapping grids, though its aeroacoustic capability in OpenFOAM has not been well established. A comparative analysis was conducted on a Joby-scale five-bladed propeller at an 80° tilt angle without a fairing, representing a transition-flight condition. Aerodynamic and acoustic predictions were obtained using hybrid DDES coupled with the Ffowcs Williams–Hawkings method. Results show that the Overset method provides improved agreement with experimental thrust and torque and captures stronger leading-edge vortices than AMI. Both methods resolve blade-vortex and blade-wake interactions. However, the Overset approach produces higher broadband noise due to stronger vortices and interpolation effects, while AMI yields smoother pressure fields and clearer tonal content. In the far field, AMI better matches experimental SPL trends. Overset shows larger first-BPF SPL errors (up to 22.5 dB vs. 5.7 dB for AMI), though OASPL differences remain small. Overall, Overset is less reliable for noise prediction and more computationally expensive.
This study highlights that rotor-rotor interactions can significantly modify both tonal and broadband noise characteristics. Continued investigation into these mechanisms is vital to developing reliable noise prediction methodologies and establishing design strategies that balance propulsion efficiency with acoustic acceptability for AAM vehicles. This work sought to answer what physical mechanisms contribute to the increased dominance of broadband noise in eVTOL-scale rotors. By characterizing the tip vortex of a single rotor, it was found that rotor-rotor interaction is highly dependent on two factors: Blade-vortex phasing and interaction duration. Characteristic vortex time scales can be correlated with increased noise. Interactions that generate increased noise have a non-linear relationship with rotor positioning. The interaction-generated noise is highly directive. This work aims to elucidate the dominant source noise mechanisms of rotor-rotor interaction noise by characterizing blade tip vortices using PIV.
Oil churning and windage power losses in dip-lubricated gearboxes can significantly affect overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are systematically validated against experimental data from the OSU Lab. Validation is carried out in two stages: first, by comparing the simulated oil free-surface shapes with experimental flow visualizations for various operating conditions; and second, by comparing total power loss across a range of rotational speeds and immersion depths. The findings confirm that qualitative comparisons of oil behavior show good agreement with experimental observations including splash generation, oil streak formation, and gear surface wetting. Furthermore, predicted power loss trends align with experiments, exhibiting exponential growth with RPM and a transition toward quadratic scaling as oil depth increases. Overall, this work highlights the capability of the numerical framework to predict both churning losses and multiphase flow behavior in gear lubrication systems, providing a foundation for future gearbox design and optimization.
Patching vulnerabilities in safety-critical domains such as automotive and aerospace is costly and complex. A small code modification can trigger a complete rebuild, producing a binary with widespread changes. This inflates patch size, complicates regression testing, and makes over-the-air (OTA) updates inefficient, as traditional binary patches often replace large portions of the executable. We present a binary rewriting–based experiment that shows the feasibility of a patch that updates only the affected bytes by computing the impact of a code change at the binary level. This produces minimal, localized patches rather than regenerated executables. The preliminary experiment shows that a single source change, which leads to thousands of modified bytes after recompilation, can be captured with only a few bytes using our method. For automotive and aerospace systems, this technique reduces patch size, conserves bandwidth, and minimizes disruption to certified software, offering a promising direction for efficient and reliable vulnerability remediation.
Military and aerospace applications have become increasingly complex real-time systems. Multi-core SoCs improve performance but create new challenges in maintaining and verifying deterministic behavior. Connected systems require exceptional security to protect code from external cyberattacks. Evolving functional safety and reliability standards that keep raising the bar mean developers need to begin comprehensive testing sooner if they are going to meet tighter design schedules. Finally, certifying these complex systems has become even more difficult. To help OEMs meet these challenges, the RISC-V architecture has been designed with unique capabilities that support reliability and security in the development of safety-critical applications. With its open instruction set architecture, modularity, and extensibility, RISC-V accelerates the design of functionally safe systems while reducing the complexity, cost, and risk associated with certification to standards like DO-178C and ISO 26262.
How engineers can ensure safety, reliability and quality in aerospace systems. Courbevoie, Île-de-France In an industry where failure is not an option and precision is paramount, aerospace manufacturers and suppliers are constantly seeking components and system solutions that deliver trusted reliability, performance, and compliance. Industry standards are a key part of achieving these high expectations, bringing together global leaders in the mobility industries to create defined, repeatable methods and consistent processes. One of these aerospace standards is AS1895 developed by SAE International - a critical standard due to the need for durable components that can withstand extreme conditions and offer high performance: high-temperature resistance, pressure sealing, and long service life with a cost-effective installation method. Leading aerospace companies such as Eaton and Honeywell have been manufacturing components that meet this standard for a long period of time.
This paper presents a reinforcement learning (RL)–based outer-loop controller for quadrotor UAV trajectory tracking and its real-world experimental validation. The proposed approach integrates RL into a standard cascaded flight-control architecture by replacing the conventional PID outer loop while retaining the onboard attitude and body-rate PID controllers. This hierarchical design preserves reliable inner-loop stabilization while leveraging RL to address nonlinear dynamics, coupling effects, and modeling uncertainty in translational motion. The controller is trained entirely in a physics-based simulation using Proximal Policy Optimization (PPO) and transferred directly to a Crazyflie quadrotor without additional tuning. Performance is evaluated through real-world figure-8 trajectory tracking experiments with varying time scales to impose increasing dynamic demands. Compared to a conventional PID outer-loop controller operating under identical conditions, the RL-based controller consistently reduces effective phase delay and achieves lower position and velocity tracking errors, particularly for aggressive trajectories. The results demonstrate robust sim-to-real transfer and highlight the potential of learning-based outer-loop control as a drop-in enhancement to classical quadrotor flight controllers.
This paper elucidates the implementation of software-controlled synchronous rectification and dead time configuration for bi-directional controlled DC motors. These motors are extensively utilized in applications such as robotics and automotive systems to prolong their operational lifespan. Synchronous rectification mitigates large current spikes in the H-bridge, reducing conduction losses and improving efficiency [1]. Dead time configuration prevents shoot-through conditions, enhancing motor efficiency and longevity. Experimental results demonstrate significant improvements in motor performance, including reduced thermal stress, decreased power consumption, and increased reliability [2]. The reduction in power consumption helps to minimize thermal stress, thereby enhancing the overall efficiency and longevity of the motor.
Durability validation of full vehicle structures is crucial to ensure long-term performance and structural integrity under real-world loading conditions. Physical test strain and finite element (FE) strain correlation is vital for accurate fatigue damage predictions. During torture track testing of the prototype vehicle, wheel center loads were measured using wheel force transducers (WFTs). In same prototype strain time histories were recorded at critical structural locations using strain gauges. Preliminary FE analysis was carried out to find out critical stress locations, which provided the basis for placement of strain gauges. Measured loads at wheel centers were then used in Multi Body Dynamics (MBD) simulations to calculate the loads at all suspension mount points on BIW. Using the loads at hard points transient analyses were performed to find out structural stress response. Strain outputs from the FE model were compared with physical measurements. Insights gained from these comparisons were used to update the model to achieve better correlation with test data. The findings of this paper establish a robust methodology for improving vehicle durability assessments by enhancing confidence in fatigue life predictions and structural performance. By integrating physical testing and FE simulations, this approach ensures accurate strain correlation and effective validation of long-term performance. It also provides a scalable framework for validating structural changes, supporting lightweight material integration, and enabling Value Analysis/Value Engineering (VAVE) initiatives to optimize cost-effectiveness and performance. This methodology strengthens simulation-driven durability development, offering valuable insights for future vehicle programs.
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