Electrical Discharges in Oil-Lubricated Rolling Contacts and Their Detection Using Electrostatic Sensing Technique

The reliability of rolling element bearings has been substantially undermined by the presence of parasitic and stray currents. Electrical discharges can occur between the raceway and the rolling elements and it has been previously shown that these discharges at relatively high current density levels can result in fluting and corrugation damages. Recent publications have shown that for a bearing operating at specific mechanical conditions (load, temperature, speed, and slip), electrical discharges at low current densities (<1 mA/mm2) may substantially reduce bearing life due to the formation of white etching cracks (WECs) in bearing components, often in junction with lubricants. To date, limited studies have been conducted to understand the electrical discharges at relatively low current densities (<1 mA/mm2), partially due to the lack of robust techniques for in-situ quantification of discharges. This study, using voltage measurement and electrostatic sensors, investigates discharges in an oil-lubricated steel-steel rolling contact on a TE74 twin-roller machine under a wide range of electrical and mechanical conditions. The results show that the discharges events between the rollers are influenced by temperature, load, and speed due to changes in the lubricant film thickness and contact area, and the sensors are effective in detecting, characterizing and quantifying the discharges. Hence, these sensors can be effectively used to study the influence of discharges on WEC formation.

On the Use of Robust Techniques in Smart Grid Control

This paper discusses the application of robust control techniques to a smart grid (SG) in order to find more powerful and suitable control tools to guarantee SG robustness. Two key aspects are in particular discussed. The first one relates to the need of a suitably model for the SG. The second one relates to the selection of an appropriate robust control technique to guarantee rejection of the adverse effects caused by mutual interactions among control loops and model uncertainty. The final purpose is to bridge the gap between the power of robust control theorems and the reality of SG operations.

Li-Rich Antiperovskite/Nitrile Butadiene Rubber Composite Electrolyte for Sheet-Type Solid-State Lithium Metal Battery

Lithium-rich antiperovskites (LiRAPs) hold great promise to be the choice of solid-state electrolytes (SSEs) owing to their high ionic conductivity, low activation energy, and low cost. However, processing sheet-type solid-state Li metal batteries (SSLiB) with LiRAPs remains challenging due to the lack of robust techniques for battery processing. Herein, we propose a scalable slurry-based procedure to prepare a flexible composite electrolyte (CPE), in which LiRAP (e.g., Li2OHCl0.5Br0.5, LOCB) and nitrile butadiene rubber (NBR) serve as an active filler and as a polymer scaffold, respectively. The low-polar solvent helps to stabilize the LiRAP phase during slurry processing. It is found that the addition of LOCB into the NBR polymer enhances the Li ion conductivity for 2.3 times at 60°C and reduces the activation energy (max. 0.07 eV). The as-prepared LOCB/NBR CPE film exhibits an improved critical current of 0.4 mA cm−2 and can stably cycle for over 1000 h at 0.04 mA cm−2 under 60°C. In the SSLiB with the sheet-type configuration of LiFePO4(LFP)||LOCB/NBR CPE||Li, LFP exhibits a capacity of 137 mAh/g under 60 at 0.1°C. This work delivers an effective strategy for fabrication of LiRAP-based CPE film, advancing the LiRAP-family SSEs toward practical applications.

Nano-structured dynamic Schiff base cues as robust self-healing polymers for biomedical and tissue engineering applications: a review

Polymer materials are vulnerable to damages, failures, and degradations, making them economically unreliable. Self-healing polymers, on the other hand, are multifunctional materials with superior properties of autonomic recovery from physical damages. These materials are suitable for biomedical and tissue engineering in terms of cost and durability. Schiff base linkages-based polymer materials are one of the robust techniques owing to their simple self-healing mechanism. These are dynamic reversible covalent bonds, easy to fabricate at mild conditions, and can self-reintegrate after network disruption at physiological conditions making them distinguished. Here we review self-healing polymer materials based on Schiff base bonds. We discuss the Schiff base bond formation between polymeric networks, which explains the self-healing phenomenon. These bonds have induced 100% recovery in optimal cases.

Passive Fault-Tolerant Control of a 2-DOF Robotic Helicopter

The presence of faults in dynamic systems causes the potential loss of some of the control objectives. For that reason, a fault-tolerant controller is required to ensure a proper operation, as well as to reduce the risk of accidents. The present work proposes a passive fault-tolerant controller that is based on robust techniques, which are utilized to adjust a proportional-derivative scheme through a linear matrix inequality. In addition, a nonlinear term is included to improve the accuracy of the control task. The proposed methodology is implemented in the control of a two degrees of a freedom robotic helicopter in a simulation environment, where abrupt faults in the actuators are considered. Finally, the proposed scheme is also tested experimentally in the Quanser® 2-DOF Helicopter, highlighting the effectiveness of the proposed controller.

Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm

Economic Load Dispatch (ELD) plays a pivotal role in sustainable operation planning in a smart power system by reducing the fuel cost and by fulfilling the load demand in an efficient manner. In this work, the ELD problem is solved by using hybridized robust techniques that combine the Genetic Algorithm and Artificial Fish Swarm Algorithm, termed the Hybrid Genetic–Artificial Fish Swarm Algorithm (HGAFSA). The objective of this paper is threefold. First, the multi-objective ELD problem incorporating the effects of multiple fuels and valve-point loading and involving higher-order cost functions is optimally solved by HGAFSA. Secondly, the efficacy of HGAFSA is demonstrated using five standard generating unit test systems (13, 40, 110, 140, and 160). Finally, an extra-large system is formed by combining the five test systems, which result in a 463 generating unit system. The performance of the developed HGAFSA-based ELD algorithm is then tested on the six systems including the 463-unit system. Annual savings in fuel costs of $3.254 m, $0.38235 m, $2135.7, $9.5563 m, and $1.1588 m are achieved for the 13, 40, 110, 140, and 160 standard generating units, respectively, compared to costs mentioned in the available literature. The HGAFSA-based ELD optimization curves obtained during the optimization process are also presented.

Parallel implementation of maximum-shift algorithm using OpenMp

String matching is considered as one of the center issues within the field of computer science, where there are numerous computer applications that supply the clients with string matching services. The increment within the number of databases which are created and protected in numerous computer gadgets had impacted researchers with the slant towards getting robust techniques in tending to this issue. In this study, the Maximum-Shift string matching algorithm is chosen to be executed with multi-core innovation through the utilization of OpenMP paradigm, in order to decrease the successive time, and increment the speedup and efficiency of the algorithm. The deoxyribonucleic acid (DNA), protein and the English text datasets are utilized to test the parallel execution that influences the Maximum-Shift algorithm execution when utilized with multi-core environment. The results demonstrated that the execution is affected by the performance between the parallel and consecutive execution of Maximum-Shift algorithm by data type. The English text appeared ideal comes about within the parallel execution time as compared to other datasets, whereas the DNA database set appeared the most elevated comes about when compared to other data types in terms of speedup and efficiency capabilities.

Deep Learning Point Cloud Odometry

Achieving persistent and reliable autonomy for mobile robots in challenging field mission scenarios is a long-time quest for the Robotics research community. Deep learning-based LIDAR odometry is attracting increasing research interest as a technological solution for the robot navigation problem and showing great potential for the task.In this work, an examination of the benefits of leveraging learning-based encoding representations of real-world data is provided. In addition, a broad perspective of emergent Deep Learning robust techniques to track motion and estimate scene structure for real-world applications is the focus of a deeper analysis and comprehensive comparison.Furthermore, existing Deep Learning approaches and techniques for point cloud odometry tasks are explored, and the main technological solutions are compared and discussed.Open challenges are also laid out for the reader, hopefully offering guidance to future researchers in their quest to apply deep learning to complex 3D non-matrix data to tackle localization and robot navigation problems.

Performance Analysis of Linearly arranged Concentric Circular Antenna Array with Low Sidelobe Level and Beamwidth Using Robust Tapering Technique.

In this research, a novel antenna array named Linearly arranged Concentric Circular Antenna Array (LCCAA) is proposed concerning lower beamwidth, lower sidelobe level, sharp ability to detect the false signal, and impressive SINR performance. The performance of the proposed LCCAA beamformer is compared with geometrically identical existing beamformers using the conventional technique where the LCCAA beamformer shows the lowest beamwidth and sidelobe level(SLL) of 12.50°and -15.17 dB in equal element accordingly. However, the performance gets degraded due to looking direction error, and robust techniques- fixed diagonal loading (FDL), optimal diagonal loading (ODL), and variable diagonal loading (VDL) are applied to all the potential arrays to minimize this problem. Furthermore, the LCCAA beamformer is further simulated to reduce the sidelobe applying tapering techniques where the hamming window shows the best performance having 17.097 dB less sidelobe level compared to the uniform window. The proposed structure is also analyzed under a robust tapered (VDL-hamming) method which reduces around 69.92 dB and 48.39 dB more sidelobe level compared to conventional and robust techniques. Analyzing all the performances, it is clear that the proposed LCCAA beamformer is superior and provides the best performance with the proposed robust tapered (VDL-hamming) technique.