Hasil untuk "Mechanical engineering and machinery"

Changchang Che, Zhixian Li, Helong Wang et al.

Accurate prediction of the remaining useful life (RUL) of rotating machinery is critical for predictive maintenance and operational safety in industries. A novel multi-step RUL prediction framework for rotating machinery is proposed in this paper, integrating variational mode decomposition (VMD) for noise reduction and an attention-based sequence to sequence (Seq2Seq) model that employs multi-head self-attention for end-to-end prediction from multi-state parameters to RUL. The VMD algorithm adaptively decomposes raw vibration signals into intrinsic mode functions (IMFs), effectively isolating noise and enhancing feature extraction by selecting dominant modes based on energy and correlation thresholds. The denoised features are then fed into a Seq2Seq architecture, in which the encoder captures temporal dependencies in the sensor data, and the decoder, augmented with multi-head self-attention, dynamically weights salient features across time steps to improve long-term forecasting accuracy. The proposed model demonstrates superior performance on both the C-MAPSS aero-engine simulation dataset and the XJTU-SY experimental rolling bearing degradation dataset, achieving mean absolute error (MAE) reductions ranging from 17.5% to 39.7% across different prediction horizons and subsets compared to the best-performing baseline. These results highlight the enhanced accuracy and robustness of the proposed approach for multi-step RUL prediction of rotating machinery, providing a reliable solution for industrial applications.

Ayoub Hachani Bouchareb, Abdel Wahhab Lourari, Bilal El Yousfi et al.

This study introduces an innovative framework for intelligent condition monitoring of bearings and gears, employing Dynamic Mode Decomposition (DMD) on multi-sensor data that includes vibration, electrical, and acoustic measurements. To our knowledge, this is the first work to explore the application of DMD for fault diagnosis in this domain. The methodology begins with a preprocessing stage, where DMD is applied to extract dynamic features that capture the intrinsic behaviors of the monitored components. From the reconstructed signals generated via DMD, a comprehensive set of statistical and time–frequency indicators is derived. To enhance diagnostic performance and minimize feature redundancy, the Sequential Backward Selection (SBS) algorithm is implemented, yielding a compact yet informative feature subset. These refined features are subsequently used as inputs to various intelligent classification models for fault detection and categorization. The proposed approach achieves an impressive diagnostic accuracy of 99.20%, demonstrating strong robustness and generalizability. Validation is carried out using four distinct datasets, two vibration-based, one acoustic, and one electrical, that cover different operational scenarios and sensor modalities. The results substantiate the effectiveness of the proposed framework in providing accurate and reliable health evaluations of the components of rotating machinery.

Bowen Li, Zhen Wang, Chuanzhen Huang et al.

Fang Chen

IntroductionAccurate speed measurement is crucial for improving the efficiency and reliability of the transmission system in hydraulic excavators. However, traditional M and T methods have their own limitations in speed measurement, especially in terms of measurement accuracy over a wide speed range.MethodsA M/T velocity measurement algorithm combining the advantages of M and T methods was proposed to address this issue, and dynamic errors were obtained. The kinetic energy theorem can also be used.ResultsThe experimental results show that under the normal load of unit tooth width F, the change rate of transmission error is less than 5% and less than 2% nM > 100N/mm.DiscussionThe monitoring method of hydraulic excavating mechanical transmission error has the advantages of high accuracy and strong adaptability, which can provide a new highway bureau for mechanical transmission error monitoring.

Hua Feng

Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana et al.

Bangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fish and struggles to recognize them. Traditional methods are very insufficient to overcome these issues. To address these research gaps, the research proposes an automatic system for categorizing Bangladesh's freshwater fish. The proposed methodology involves several key steps, including building a comprehensive dataset, extracting features from fish images using pre-trained Convolutional Neural Network (CNN) models, and employing typical ML approaches. Initially comprising eight classes, the dataset undergoes feature extraction using CNN algorithms, followed by the utilization of various feature selection methods such as Support Vector Classifier, Principal Component Analysis, Linear Discriminant Analysis, and optimization models like Particle Swarm Optimization, Bacterial Foraging Optimization, and Cat Swarm Optimization. In the final phase, seven conventional ML techniques are applied to classify the images of local fishes. Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models.

M. Ravikumar

Aluminum composites with varied weight percentages of 0-2.5 B4C particles and micro- and nanoparticle sizes were fabricated by stir-casting. The material's mechanical and wear characteristics were evaluated. We used dry pin-on-disc wear testing to examine the wear behavior of both micro and nano composites. In the sliding wear trials, different particle sizes (micro and nano), sliding distances (1500 m and 3000 m), and sliding speeds (3 m/s and 6 m/s) were employed. Scanning Electron Microscope (SEM) was utilized in the experiment to examine the materials and microstructures of several composites. Uniform dispersion of the micro and nano particles was readily evident in the SEM image. B4C particle microhardness increased by 16.06 % in nano composites and 10.78 % in micro composites. In a similar way, B4C particles' tensile strength increased by 12.90% in nano composites and 8.78% in micro composites. Taguchi design for experimental technique was applied to a L8 orthogonal array in order to design and ascertain the effects of sliding distance, sliding speed, and particle size on dry sliding wear behavior. ANOVA study showed that the most significant influencing factor on wear resistance was particle size (61.29%), followed by sliding speed (17.27%) as well as sliding distance (14.20%). From the confirmatory tests, the Coefficient of Friction (COF) of the produced composites had a maximum error of 9.09 % and the error of 3.33 % was found in the wear rate which was within the acceptable limit. The wornout surface shows that the composite reinforced with nanoparticles has a smooth wear surface with a finer wear scar.

Satoshi Otsuki, Miki Taya, Kenichi Nakashima et al.

This paper presents a novel hierarchical control architecture designed for navigating multiple tugboats to perform complex maneuvers, including approaching, enclosing, and capturing a large target vessel, while taking into account external disturbances and operational constraints. The proposed hierarchical control architecture includes a high-level nominal controller for trajectory generation, tracking, and coordination, and a low-level model-following controller for multitask execution. The low-level controller integrates constraint-driven control to refine nominal control inputs generated by the high-level controller, and local control to mitigate disturbances. To achieve multitask execution across all phases, the constraint-driven controller activates only essential constraints, enforcing seamless maneuver transitions without relying on ad-hoc controller switching. Specifically, we design constraints to facilitate effective enclosing behaviour while guiding the tugboat fleet toward the target in formation. We also adopt a so-called Prescribed-Time Safety Filter to enforce mild contact with the surface of the target vessel within a specified finite-time interval. Simulation studies validate the proposed control architecture across all maneuvers throughout the operation and demonstrate its capability to achieve complex multi-tugboat coordination.

Sheikh Noman Shiddique, Ahnaf Tahmid Abir, Md Jayed Hossain et al.

In this article, we demonstrate CdSe–CuSbSe2-based double junction two-terminal tandem solar cells simulated with SCAPS-1D. The highest performance of the tandem cell has been confirmed by optimizing the electrical and optical properties of the window, top absorber, CdSe (bandgap 1.7 eV), bottom absorber, CuSbSe2 (bandgap 1.08 eV), and back surface layers. In addition, the effect of different parameters such as thickness, doping, and defect density of different layers has been investigated in detail. With the optimized condition, the modeled CdSe–CuSbSe2 double-junction two-terminal tandem solar cell displays a noticeable efficiency of 42.64% with an open-circuit voltage of 2.09 V, short-circuit current density of 24.09 mA/cm2, and fill factor of 84.36%, respectively. These results are highly propitious for the construction of all-chalcogenide–based high-performance tandem photovoltaic cells in the future.

Benji Zhou, Nengneng Xu, Liangcai Wu et al.

Fabricating non-noble metal-based carbon air electrodes with highly efficient bifunctionality is big challenge owing to the sluggish kinetics of oxygen reduction/evolution reaction (ORR/OER). The efficient cathode catalyst is urgently needed to further improve the performance of rechargeable zinc-air batteries. Herein, an activation-doping assisted interface modification strategy is demonstrated based on freestanding integrated carbon composite (CoNiLDH@NPC) composed of wood-based N and P doped active carbon (NPC) and CoNi layer double hydroxides (CoNiLDH). In the light of its large specific surface area and unique defective structure, CoNiLDH@NPC with strong interface-coupling effect in 2D-3D micro-nanostructure exhibits outstanding bifunctionality. Such carbon composites show half-wave potential of 0.85 V for ORR, overpotential of 320 mV with current density of 10 mA cm−2 for OER, and ultra-low gap of 0.70 V. Furthermore, highly-ordered open channels of wood provide enormous space to form abundant triple-phase boundary for accelerating the catalytic process. Consequently, zinc-air batteries using CoNiLDH@NPC show high power density (aqueous: 263 mW cm−2, quasi-solid-state: 65.8 mW cm−2) and long-term stability (aqueous: 500 h, quasi-solid-state: 120 h). This integrated protocol opens a new avenue for the rational design of efficient freestanding air electrode from biomass resources.

Chen Wu, Zhinong Wei, Yang Cao et al.

Abstract The increasing amount of distributed renewable energy (DRE) is participating in grid‐connected operation as an important unit of the virtual power plant (VPP) aggregation. VPP also contains a variety of flexible resources such as demand response (DR), energy storage (ES), and fuel cell (FC). How to achieve efficient energy utilization while reducing carbon emissions and resisting the risk of failure caused by extreme weather has attracted widespread attention. In this article, a cooperative game‐based low‐carbon scheduling model for multi‐VPPs under the consideration of typhoon‐induced grid outage risks is proposed. First, a cooperative game mechanism for multi‐VPPs is constructed. And a bi‐level model of multi‐VPPs low‐carbon scheduling is built under the framework of electricity‐carbon trading markets. Second, the bi‐level scheduling model is linearized based on the Strong Duality Theorem and Karush‐Kuhn‐Tucker (KKT) condition. Then, the dispatch scheme of each VPP under the cooperative game form is obtained. Finally, simulations are performed to verify the validity of the proposed model. The results show that the economic and low‐carbon performance of multi‐VPPs can be improved by applying the cooperative game, which can also enhance the power system ability of resisting line faults.

Zelin Zhang, Yu Qin, Jie Zhu et al.

We optimized and fabricated an ultra-bend-resistant 4-core simplex cable (SXC) employing 4-core multicore fiber (MCF) suitable for short-reach dense spatial division multiplexing (DSDM) optical transmission in the O-band. The characteristics of transmission loss, macro-bending and cross-talk (XT) between adjacent cores after cabling were firstly clarified. By introducing the trapezoid index and optimizing the cabling process, the maximum values of added XT of 1.17 dB/km due to 10 loops with a bending radius of 6 mm imposed over the 4-core SXC and a macro-bending loss of 0.37 dB/10 turns were, respectively, achieved.P Then, the optical transmission with low bit error rate (BER) was presented using a 100GBASE-LR4 transceiver over the 1.2 km long 4-core SXC. The excellent bending resistance of the 4-core SXC may pave the way for a reduction in space pressure and increase in access density on short-reach optical interconnect (OI) based on DSDM.

A. D. Chaudhari, P. Bhupathi, V. V. Joshi

This paper explores the potential of a cylindrical enclosure with vent holes to create and maintain the desired thermal environment for indoor farming. Different thermal zones can be made in a single room when such enclosures are used in multiple numbers in a single room. A comparative analysis of twelve different air cooling/heating configurations was conducted. Each cylindrical enclosure is air-filled, with two heat sinks facing each other and vent holes in the top and bottom surfaces. Six configurations had heat sinks oriented vertically, and the other six had heat sinks inclined at 45°. These configurations (vertical and inclined heat sinks) have been studied for different heat sink temperatures and sidewall heat flux conditions. The numerical simulations were conducted using ANSYS-Fluent. The studies have shown that different thermal environments can be created inside the enclosure, and cooling can be achieved with sufficient air exchange through vent holes. The instabilities due to buoyancy-driven flow are found to be necessary for air exchange through vent holes. Validation studies have shown that the heat flux from the sidewall should be considered, even if it is an excellent thermal insulator.

Lidia Gałda, Dariusz Pająk

The paper presents results obtained by SiC ceramic implementation for slide burnishing. Analyzes of the effect of the burnishing force and feed on selected surface texture parameters were conducted. The influence of technological parameters was assessed according to the analysis of variance. The burnishing force and feed affected most of the examined surface texture parameters in the analyzed ranges. Optimal values were found for the technological parameters. In most cases, the minimum of height parameters were achieved for 80 N of burnishing force and 0,063 mm/rev of feed. Limitations in the increase in the burnishing force were also found.

Mohamed Mostafa Elsaied, Walid Helmy Abdel Hameed, Hany M. Hasanien

Abstract This paper presents a three‐area system tied together with tie lines. During the intervals of power mismatch, the system frequency deviates beyond the nominal value with an oscillatory response and the system may go to instability mode. The main purpose of the control strategy is optimizing the frequency fluctuations of the three areas and the deviations in the three tie line powers. This is achieved by three controllers as follows: the Tilt‐Integral‐Derivative (TID) controller, the Fractional Order Proportional‐Integral‐Derivative (FOPID), and the Proportional‐Integral‐Derivative (PID) controller. The three controllers are optimized by a new metaheuristic optimization algorithm based on the jellyfish behaviour in the ocean called the Jellyfish Search (JS) Optimizer. To prove the algorithm validity, it is compared with previous optimization techniques that have been applied to the study field such as Grey Wolf Optimization (GWO) algorithm and Genetic Algorithm (GA). Furthermore, renewable energy sources are implemented in the system such as wind energy and photovoltaic based on real data. Finally, energy storage devices (ESDs) like superconducting magnetic energy storage (SMES), capacitor energy storage (CES), and battery energy storage (BES) are implemented to improve the system behaviour due to the intermittent behaviour in the renewable sources.

Fauzi Yusupandi, Hary Devianto, Pramujo Widiatmoko et al.

Intermediate temperature solid oxide fuel cell (IT-SOFC) provides economic and technical advantages over the conventional SOFC because of the wider material use, lower fabrication cost and longer lifetime of the cell components. In this work, we fabricated electrolyte-supported IT-SOFC using low-cost materials such as calcia-stabilized zirconia (CSZ) electrolyte fabricated by dry-pressing, NiO-CSZ anode and Ca3Co1.9Zn0.1O6 (CCZO) cathode produced through brush coating technique. According to the XRD result, the monoclinic phase dominated over the cubic phase, and the relative density of the electrolyte was low but the hardness of the CSZ electrolyte was close to the hardness of commercial 8YSZ electrolyte. The performance of the single cell was performed with hydrogen ambient air. An open-circuit voltage (OCV) of 0.43, 0.46, and 0.45 V and a maximum power density of 0.14, 0.50, and 1.00 mW/cm2 were achieved at the operating temperature of 600, 700, and 800 °C, respectively. The ohmic resistance of the cell at 700 and 800 °C achieved 81.5 and 33.00 Ω, respectively due to the contribution of thick electrolyte and Cr poisoning in electrodes and electrolyte

Giulia Perugia, Maike Paetzel-Prüsmann, Madelene Alanenpää et al.

Over the past years, extensive research has been dedicated to developing robust platforms and data-driven dialog models to support long-term human-robot interactions. However, little is known about how people's perception of robots and engagement with them develop over time and how these can be accurately assessed through implicit and continuous measurement techniques. In this paper, we explore this by involving participants in three interaction sessions with multiple days of zero exposure in between. Each session consists of a joint task with a robot as well as two short social chats with it before and after the task. We measure participants' gaze patterns with a wearable eye-tracker and gauge their perception of the robot and engagement with it and the joint task using questionnaires. Results disclose that aversion of gaze in a social chat is an indicator of a robot's uncanniness and that the more people gaze at the robot in a joint task, the worse they perform. In contrast with most HRI literature, our results show that gaze toward an object of shared attention, rather than gaze toward a robotic partner, is the most meaningful predictor of engagement in a joint task. Furthermore, the analyses of gaze patterns in repeated interactions disclose that people's mutual gaze in a social chat develops congruently with their perceptions of the robot over time. These are key findings for the HRI community as they entail that gaze behavior can be used as an implicit measure of people's perception of robots in a social chat and of their engagement and task performance in a joint task.

Cho W. S. To

Dingcheng Zhang, Dejie Yu, Xing Li

The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.