Search

Rotary encoders are commonly used for dynamic control and positioning of industrial robots. Results of this study suggested that rotary encoder signal can also be used to monitor the efficiency of industrial robot systems effectively after proper processing. A novel strategy using singular spectrum analysis (SSA) integrated with hierarchical hyper-Laplacian prior prototype (HHLP) is proposed in this study for defect detection in industrial robots. Sparsity-assisted techniques are efficient flaw-based extraction techniques that have been extensively investigated in recent years. However, the selection of an appropriate sparse prior from the point of view of probability theory remains unverified. First, SSA enables the separation of complicated encoding signals to several interpretable components, including a trend, a set of cyclic oscillations, and residual oscillations (noise). Second, we describe HHLP by maximizing posterior probability of robot flaw diagnosis. HHLP is proposed to extract noise interference, cyclic pulse, and harmonic interference from the residual signal using the SSA technique. We infer that the hyper-Laplacian prior in the prototype may present a more efficient prototype flaw than the Laplacian prior. In addition, HHLP incorporates physical characteristics necessary to distinguish harmonic intervention. This study primarily establishes a modern prototype that represents the former dispersed from the perspective of maximizing the probability of the latter. Meanwhile, generalized minimax-concave regularization inductive and kurtosis-based weighted sparse prototypes are compared and spectral kurtosis is used to confirm the efficacy of HHLP. Note to Practitioners—This study aims to solve the problem of industrial robot fault diagnosis during the operation process to avoid production delays. Rotary encoder sensor is attached to each joint to collect raw information and identify the robot position. Data from the rotary encoder sensor can also be used for the efficient health status assessment of the performance of the industrial robot system after proper processing. Therefore, a new approach using singular spectrum analysis combined with hierarchical hyper-Laplacian pre-induced prototype is proposed in this study. The residual signal extracted using the singular spectrum analysis method for processing in a hierarchical hyper-Laplacian pre-induced prototype is used to improve the weak fault feature. We describe a hierarchical hyper-Laplacian preprototype by maximizing the posterior probability of the flaw diagnosis. We introduce a hierarchical hyper-Laplacian prior that integrates physical characteristics to distinguish between harmonic interferences. The distribution of coefficients acquired through other dictionaries or transformations and selection of the optimal priority for the extraction of flaw characteristics will be the foci of future investigations. [ABSTRACT FROM AUTHOR]

The optimization of the energy management of modern hybrid-electric or fully electric race cars for minimum lap time requires a description of the vehicle dynamics performance envelope, that is, of the tires’ grip limit in corners, braking zones and during acceleration. In this paper, we present a computationally efficient performance envelope model in the form of convex constraints on the achievable longitudinal and lateral acceleration, on the assumption that the path on the track is given. The proposed acceleration limits are modeled velocity-dependent to take into account the effect of aerodynamic downforce present in many circuit race cars. The formulation as linear equality, inequality and second-order cone constraints allows to embed the model in a convex energy management optimization framework. To showcase the approach, we identify the model with data obtained from a state-of-the-art hybrid-electric Formula 1 car and present results for the Silverstone and Spa-Francorchamps circuits. The optimal energy management strategies can be evaluated with a computational time of less than 1 s. The optimal velocity profile subject to the performance envelope constraints is close to the measured one. The good agreement between the optimal solution and the measurement data shows that the proposed model captures the vehicle dynamics accurately enough for the purposes of energy management optimization. [ABSTRACT FROM AUTHOR]

With the introduction of stricter energy-efficiency-ratio regulations, the surface-mounted permanent-magnet motor (SPM) is gaining increasing attention due to its inherent high-power density. This article investigates the feasible SPM structures for refrigerant compressor applications, particularly those used in metal industry factories with high surrounding temperatures so that cost-effective solutions can be achieved. A theoretical model is first established to aid structural parameter optimizations and the management of specific operational objectives and constraints; multiple SPM arrangements are investigates, with ease of manufacturing implementation being one consideration, as shown in Liu et al., 2021. Detailed finite element calculations and experimental measurements are performed to develop feasible SPMs with optimized structural arrangements that meet the desired operational objectives. [ABSTRACT FROM AUTHOR]

Federated Learning (FL) over wireless network enables data-conscious services by leveraging the ubiquitous intelligence at network edge for privacy-preserving model training. As the proliferation of context-aware services, the diversified personal preferences cause disagreeing conditional distributions among user data, which leads to poor inference performance. In this sense, clustered federated learning is proposed to group user devices with similar preference and provide each cluster with a personalized model. This calls for innovative design in edge association that involves user clustering and also resource management optimization. We formulate an accuracy-cost trade-off optimization problem by jointly considering model accuracy, communication resource allocation and energy consumption. To comply with parameter encryption techniques in FL, we propose an iterative solution procedure which employs deep reinforcement learning based approach at cloud server for edge association. The reward function consists of minimized energy consumption at each base station and the averaged model accuracy of all users. Under our proposed solution, multiple edge base station are fully exploited to realize cost efficient personalized federated learning without any prior knowledge on model parameters. Simulation results show that our proposed strategy outperforms existing strategies in achieving accurate learning at low energy cost. [ABSTRACT FROM AUTHOR]

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding. [ABSTRACT FROM AUTHOR]

Meeting the stringent performance requirements for power electronic converters in electric vehicles requires an integrated approach for optimizing the inherently coupled electrical and thermal performances of converter systems. This article presents a multidisciplinary thermal management design methodology that utilizes genetic algorithms (GAs) to generate topologically optimized geometries for liquid-cooled heat sinks. These GA-generated heat sinks are based on impingement cooling principles and leverage the flexibility of stereolithographic manufacturing techniques. The proposed optimization methodology incorporates the interdependence between the thermal and electrical aspects of the system, and it is capable of targeting performance metrics in either or both domains. This optimization process is demonstrated for a 6.6-kW integrated power module design employing bare-die silicon carbide devices on an FR4-based printed circuit board with embedded ceramic elements. Experimentally validated electrothermal multiphysics simulations of the GA-optimized heat sinks targeting various performance metrics show successful optimization of targeted metrics relative to the initial seed design. The results demonstrate the importance of the multidisciplinary design approach and the effectiveness of the GA-based optimization methodology. [ABSTRACT FROM AUTHOR]

Sizing optimization and energy management strategy (EMS) are two key points for the application of the hybrid energy storage system (HESS) in electric vehicles. This article aims to conduct the sizing optimization of HESS and apply an adaptive real-time EMS for practice. First, considering the system cost and battery lifespan, the multiobjective grey wolf optimizer is used to obtain the Pareto front. Second, with optimal parameters, the offline optimal power splitting results by dynamic programming (DP) under different driving patterns are analyzed. Then, the random forests (RF) method is used to learn control rules from the DP results. Driving pattern recognition (DPR) is implemented by the support vector machine (SVM). The intelligent EMS is composed of RF to guide power distribution and SVM to realize DPR. Finally, a combined load cycle involving different driving patterns is used for verification. Results illustrate that the proposed adaptive RF-based EMS can demonstrate a notable superiority in terms of battery protection, ultra-capacitor utilization, and system efficiency. Compared with the ordinary RF-based EMS without DPR, the proposed method can reduce total energy loss by 0.74%–9.49%, and reduce the battery Ah-throughput by 0.5%–19.83% under unknown driving cycles. [ABSTRACT FROM AUTHOR]

This article proposes a multilevel coordinated voltage control (CVC) strategy for facilitating distributed residential demand response for network voltage optimization. The proposed CVC scheme coordinates multiple home energy management systems (HEMS) connected on a low-voltage radial distribution network through a sensitivity-based approach to minimize voltage deviations in multiple timescales owing to high photovoltaic (PV) penetrations and load variability. In order to mitigate the effects of uncertain PV output and load during actual system operation, a day-ahead two-stage robust optimization (TSRO) model has been developed, which optimizes network operation in the longer timescale, for worst-case realization of uncertainties in shorter timescales. This is sequentially followed by fully distributed hour-ahead CVC which is decomposed into network optimization and energy management subproblems through alternating direction method of multipliers. In order to counteract the effects of real-time uncertainties, local adjustments are performed by redispatching HEMS at individual households to locally adapt to unforeseen variations, thereby minimizing real-time voltage deviations. Extensive simulations are performed on the modified Dutch LV network. Simulation results demonstrate effectiveness of the proposed multilevel CVC approach to robustly handle uncertainties while ensuring reliable operation and maximum user-comfort. [ABSTRACT FROM AUTHOR]

Welding defect detection based on radiographic images plays a vital role in industrial nondestructive testing. It provides an effective guarantee with respect to welding quality in shipbuilding, chemical industry, and aerospace applications. A variety of related computer-based image processing technologies have been designed for the detection of weld defects. However, this is a challenging task because weld defects can exhibit different shapes, sizes, positions, and contrasts in radiographic images. This article proposes an end-to-end weld defect recognition method that mainly includes three steps. In the first step, we propose an improved algorithm based on deep belief network (DBN), which classifies weld feature curves extracted by applying infinite norm for detecting defective weld base on radiographic images. In the second step, a new cylindrical projection method is proposed to increase the proportion of defect parts in the images and solve the problem of loss of defects with small size. And in the third step, we propose an improved deep learning network that is based on SegNet to identify weld defects. Experimental verification shows that this method can realize end-to-end weld defect recognition and strong robustness. Compared with existing methods, this method exhibits obvious advantages and can effectively assist inspectors in welding defect detection and significantly improve the detection efficiency of industrial nondestructive testing. [ABSTRACT FROM AUTHOR]

Industrial digital transformation through efficient automation hinges largely on the deployment of communication infrastructures that meet the requirements of smart factory use cases. These infrastructures involve multiple devices that utilize different communication technologies to increase the overall operational efficiency. Rooting from the key implementation requirements of a smart factory environment, this article focuses on the role of cellular connectivity and wearable technology in enabling new industrial applications. Specifically, we shed light on a novel category of services - industrial mid-end wearable applications - by positioning their requirements among the 5G service classes. We then identify features that can complement cellular connectivity to further support the given requirements. More precisely, we review cellu-lar-network-aided device-to-device communications and reduced-capability devices. Our performance evaluation results justify the choice of these features and show that they can work in concert with cellular connectivity to enhance spectral efficiency and reliability in industrial mid-end wearable applications. [ABSTRACT FROM AUTHOR]

Integrated terrestrial-satellite networks (lTSNs) are envisioned to provide seamless broadband services through evolving terrestrial B5G/6G cellular systems and emerging mega satellite constellations. The inherent dual mobility (i.e., satellites and ubiquitous mobile users) and highly overlapped satellite footprints in such ITSNs may result in massive and frequent handovers. This increases handover delays, signaling overheads, and decision making loads, especially under existing fixed, even limited, deployments of ground mobility management functions (MMFs). Thus, a lightweight handover scheme and a mobility management architecture with dynamic MMF configurations are of great significance to guarantee service continuity and management timeliness. Specifically, this article proposes mobility management architectures with an augmented reconfigurable management plane on a higher orbit. The architectures can support flexible configurations of space-distributed MMFs for efficient mobility management together with the ground MMFs. Subsequently, to achieve rapid and unified decisions for massive handovers, we design a clustering and game-based handover decision framework including centralized and distributed decision making functions for different conditions. Based on handover procedures, simulation results validate the high performance of the proposed schemes regarding handover delays, signaling overheads, and convergence property. Notably, many academic issues are worthy of further studies under this architecture, such as higher-layer constellation design and inter-layer management structure optimization. [ABSTRACT FROM AUTHOR]