Browse Topic: Reliability
Tackling the heavy computation of affine formation control under switching topologies—rooted in frequent stress matrix recalculation—this paper presents a distributed control framework fusing consistency estimation with dynamic error constraints for efficient coordination. In a leader-follower architecture, affine transformation parameters are estimated by followers using local information—global stress matrix solutions are thus avoided. A time-varying constraint function and Lyapunov stability analysis are devised to ensure tracking errors converge to specified accuracy within a predetermined time. Both theoretical analysis and simulation results show that this method greatly simplifies computation. It also supports flexible formation transformations such as translation and scaling, making it a stable and reliable solution for dynamic scenes.
In the conversation surrounding electrification, the vehicle itself typically dominates the headlines. But those operating on remote jobsites in the mining, construction and agriculture sectors know the machine is only half the equation. These industries prioritize reliability and uptime and require machines that can handle grueling shifts in demanding environments without compromise. Power providers in these heavy-duty, off-highway markets must move beyond the battery itself to explore a holistic approach to infrastructure when it comes to powering remote jobsites. The first step to success is understanding the fundamental differences between off-highway duty cycles and on-highway applications. While on-highway applications like long-hauling trucks benefit from steady-state operation and passive airflow for cooling, off-highway machines often operate at high torque for extended periods, with little to no forward movement. In these scenarios, there is no passive cooling to rely on or regular refueling stations at the next exit. Success, therefore, is defined by the engineering required to ensure that electric machines deliver the same productivity as diesel, even when operated at their limits in the most rugged, remote conditions.
For decades, hydraulic systems have been relied upon to do all the heavy lifting in aerospace. They are powerful, reliable, and deeply embedded in how aircraft are designed, to the extent that - for many engineers - they are simply part of the landscape. Now, however, things are beginning to change. From advanced air mobility platforms now entering certification to next-generation commercial aircraft on 10-year horizons, electric and electro-hydraulic actuation is steadily replacing the heavy, centralized hydraulic architectures that have defined flight control for decades. Understanding why means stepping back from the actuator itself and looking at the aircraft as a whole system - and, increasingly, as an integrated motion control challenge.
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.
Medical device manufacturing is undergoing a structural shift. As devices become smaller with broader functionality, traditional approaches to assembling electronics are no longer sufficient. Increasingly, performance, durability, and reliability are dictated not just by design, but by how that design is manufactured.
Robotic manipulation remains one of the harder unsolved problems in automation engineering. Vision-based systems have matured considerably — object localization, pose estimation, and grasp planning from RGB-D data are now reliable enough for structured industrial environments. What vision cannot provide is contact information: whether a grasp is stable, whether a surface is beginning to slip, or how force is distributed across a fingertip during a hold. These signals are what close the control loop during manipulation, and without them, systems compensate through excessive grip force, conservative motion profiles, and large training datasets designed to paper over sensing uncertainty.
Traditional industrial robotics has been built on traditional premises: define the task precisely, program the motion, and repeat it with minimal variation. This model has delivered reliability, speed, and scale across multiple application domains.
A new DC-DC power converter is superior to previous designs and paves the way for more efficient, reliable, and sustainable energy storage and conversion solutions. The Kobe University development can efficiently interface with a wide range of energy sources while enhancing system stability and simplicity at an unprecedented efficiency.
Designing the next generation of wireless wearables, implantables, and real-time external or in-body monitoring devices is a complex challenge that requires highly reliable electronic components that greatly exceed the performance of standard commercial alternatives.
Ultra-miniature sensors are enabling advanced procedures and treatments across a wide range of medical devices, from catheters and neuro interfaces to wearables. But as electromagnetic sensors get smaller, trade-offs begin to emerge — lower sensitivity, less tolerance for environmental influences, and greater susceptibility to interference — underscoring the need for robust testing to ensure accurate, reliable tracking.
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.
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