Aiming at the issues of under-maintenance or over-maintenance in preventive maintenance of DSA200 type pantograph, a method was proposed to optimize inspection and maintenance parameters by using pantograph failure data.Firstly, the failure datas of the pantograph components were analyzed by using graph parameter method, which failure time distribution models were fitted.The failure datas were preliminarily determined to obey the exponential distribution，and the Bartlett value method was further used to verify the validity of the failure data obey exponential distribution.Secondly, based on the structure and working characteristics of pantographs, a reliability block diagram model with pantograph components in series was constructed.According to the characteristics of constant failure rate of pantograph components, the failure rate of pantographs was obtained.Thirdly, the minimum cost model of preventive maintenance and replacement of pantographs was established, and the optimal preventive maintenance interval and the optimal number of spare parts were obtained.Finally, the structure importance, probability importance and critical importance of pantograph components were analyzed by using fault tree analysis method, and the failure probability of pantograph and the key components in inspection and maintenance were obtained.The optimized pantograph inspection and maintenance parameters can provide scientific reference for maintenance personnel to improve their maintenance level and reduce maintenance costs.
Mechanical engineering and machinery, Materials of engineering and construction. Mechanics of materials
Graphene quantum dots (GQDs), as an emerging class of nascent carbon-based materials, demonstrate remarkable promise in fluorescence sensing applications. Those potentials stem from several factors, including their favorable photoluminescence (PL) characteristics, feasibility of surface functionalization, excellent biocompatibility, and low cytotoxicity. This review concentrates on the fundamental optical properties of GQDs, with specific reference to the manipulation of intrinsic characteristics both by heteroatom doping and surface/edge functionalization. These modifications permit the alteration of optical properties, thereby rendering GQDs more versatile for an array of applications. Subsequently, we then delve into the recent applications of GQDs in fluorescence sensing, encompassing both turn-off and turn-on mechanisms. Finally, it presents a systematic assessment of the current state of research on GQDs, along with discussions on challenges and prospects for expanding and improving their applications.
Se presenta un análisis cuantitativo de las distribuciones de palabras en mensajes anuales al congreso durante más de cincuenta años, abarcando ocho períodos presidenciales en Chile y un período de liderazgo militar. Hemos evaluado los rankings de palabras y las distribuciones de su frecuencia de repetición o espaciamiento inter-palabra, así como el cumulante de cuarto orden o de Binder. Suponemos que el espaciamiento entre palabras implica un orden estructural similar a los marcadores de ADN que organizan el texto a través del trabajo del autor quien elige, pero aun así dentro del lenguaje que le permite. Encontramos evidencia de cambios en las distribuciones de las palabras más frecuentes que pueden ser responsables de la desviación de la ley de Zipf, previamente reportada para el idioma español. Este trabajo es una restauración de los textos y compilación más extensa de los recursos disponibles como mensajes al congreso y al pueblo chileno cada año desde 1973, y su aplicación tiene el potencial de optimizar el corpus de palabras, tomando distribuciones intrínsecas extraídas desde cantidades estadísticas más frecuentes basadas en el propio corpus según ranking.
Mechanical engineering and machinery, Industrial engineering. Management engineering
The belt conveyor is susceptible to longitudinal tearing, which poses a serious threat to the safety of coal mines. Traditional methods for detecting longitudinal tears have limitations such as poor image quality, limited applicability, and high hardware costs. An improved encoder-decoder network was proposed to solve the longitudinal tear detection problem. This method utilizes a line structured light system for image acquisition. The input images are downscaled using a sorting algorithm to extract the information of pixels with high grayscale values as the input feature map for the neural network. The reduced-dimensional encoder-decoder network then semantically segments the input feature map, and the resulting pixel segmentation is mapped to the location of the longitudinal tear. Finally, the position and length of the tear are calculated by back-projecting the semantic segmentation result to the world coordinate system. Experimental results demonstrate that this method effectively reduces hardware resource consumption and improves detection speed. The DICE and MIOU scores for the improved network are 97.69% and 95.47%, respectively, while the recall and precision for improved detection are 96.60% and 95.67%, respectively. Therefore, this method can successfully monitor longitudinal tear failures and ensure the safety of transportation.
Control engineering systems. Automatic machinery (General), Technology (General)
Bashra Kadhim Oleiwi, Mohamed Jasim, Ahmad Taher Azar
et al.
The position and trajectory tracking control of rigid-link robot manipulators suffers from problems such as poor accuracy, unstable performance, and response caused by unidentified loads and outside disturbances. In this paper, three control structures have been proposed to control a multi-input, multi-output coupled nonlinear three-link rigid robot manipulator (3-LRRM) system and effectively solve the signal chattering in the control signal. To overcome these problems, three hybrid control structures based on combinations between the benefits of fractional order proportional-integral-derivative operations (FOPID) and the benefits of neural networks are proposed for a 3-LRRM. The first hybrid control scheme is a neural network- (NN) like fractional order proportional-integral plus an NN-like fractional order proportional derivative controller (NN-FOPIPD) and the second control scheme is an NN plus FOPID controller (NN + FOPID). In contrast, the third control scheme is the Elman NN-like FOPID controller (ELNN-FOPID). The bat optimization algorithm (BOA) is applied to find the best parameter values of the proposed control scheme by minimizing the performance index of the integral time square error (ITSE). MATLAB software is used to carry out the simulation results. Using the simulation tests, the performance of the suggested controllers is compared without retraining the controller parameters. The robustness of the designed control schemes’ performance is assessed utilizing uncertainties in system parameters, outside disturbances, and initial position changes. The results show that the NN-FOPIPD structure demonstrated the best performance among the suggested controllers.
Mechanical engineering and machinery, Electronic computers. Computer science
Abstract Only 32 countries in the world have geothermal power plants in operation, with a combined capacity of 16,318 MW installed in 198 geothermal fields with 673 individual power units. Almost 37% of those units are of flash type with a combined capacity of 8598 MW (52.7% of total), followed by binary ORC type units with 25.1% of the installed capacity. The select list of geothermal power countries continues to be headed by the US, followed by Indonesia, the Philippines and Türkiye, and generated 96,552 GWh of electricity, at an average annual capacity factor of 67.5%, which represented 0.34% of the worldwide electric generation. Electricity from geothermal origin represented more than 10% of the total generated in at least seven countries, headed by Kenya, Iceland, and El Salvador. Practically, all geothermal fields in operation are harnessing resources from hydrothermal, conventional reservoirs, through an estimate of 3700 production wells at an annual average production of almost 3 MWh per well. Things could be similar in the next few years if the current trend continues, but all can change due to the world urgency to maintain global warming below the 1.5 °C threshold in the following years.
AbstractIn the drilling process, the adjustment of drill pipe rotational speed is essential to accommodate the variations encountered in different geological strata. However, the existing low automation level in drilling rigs and heavy operator reliance often result in suboptimal speed regulation, leading to low drilling efficiency. An adaptive control approach for power head speed based on a fuzzy clustering algorithm was proposed, which utilizes the fuzzy clustering algorithm (FCM) to cluster drilling parameters, including drilling pressure, rotational speed, and drilling speed, to identify of distinct strata. Furthermore, the rotational speeds of the clustering centers of different strata were used as the input rotational speeds of its control system, the simulation results show that the speed control can be realized by controlling the opening of the valve port, and the fuzzy PID control is used to realize the adaptive control of the speed and the tracking of the speed curve.
AbstractWhen selecting technology for transmission in the process of product development and design, various methods available for choose ideal solution from many different technical alternatives. However, this does not guarantee that a very good developed product will achieve ideal market result. This is often because there is a difference between the functionalities provided by the product and the actual customer demands. This article explores the use of the KANO model to analyze customer requirements in the early stages of product development. By categorizing customer needs and selecting the ideal solution based on different categories of customer needs, it ensures that the product development aligns with customer demands.
AbstractObtaining the aerodynamic parameters of a ballistic-correction bullet is crucial for improving ballistic correction efficiency and enhancing shooting accuracy. In this study, a numerical simulation method is utilized to analyze and calculate the aerodynamic characteristics of the bullet. The focus is on investigating the changes in aerodynamic parameters when the rudder wings are in both unexpanded and expanded states, as well as analyzing the impact of deflection angles on the bullet's aerodynamic characteristics. The simulation results demonstrate that the bullet exhibits favorable aerodynamic performance and static stability when the rudder wings are unexpanded, leading to improved ballistic correction efficiency during flight. Moreover, the results indicate that in the expanded state of the rudder wings, increasing the deflection angle enhances the impact on the bullet's aerodynamic characteristics, resulting in more pronounced changes and stronger correction capabilities. This research provides valuable insights into the aerodynamic characteristics of ballistic-correction bullets.
Al-Zubaidi Salah, Ghani Jaharah A., Haron Che Hassan Che
et al.
Titanium alloys are broadly used in the medical and aerospace sectors. However, they are categorized within the hard-to-machine alloys ascribed to their higher chemical reactivity and lower thermal conductivity. This aim of this research was to study the impact of the dry-end-milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. This research aims to study the impact of the dry-end milling process with an uncoated tool on the produced surface roughness of Ti6Al4V alloy. Also, it seeks to develop a new hybrid neural model based on the training back propagation neural network (BPNN) with swarm optimization-gravitation search hybrid algorithms (PSO-GSA). Full-factorial design of the experiment with L27 orthogonal array was applied, and three end-milling parameters (cutting speed, feed rate, and axial depth of cut) with three levels were selected (50, 77.5, and 105 m/min; 0.1, 0.15, and 0.2 mm/tooth; and 1, 1.5, and 2 mm) and investigated to show their influence on the obtained surface roughness. The results revealed that the surface roughness is significantly affected by the feed rate followed by the axial depth. A 0.49 µm was produced as a minimum surface roughness at the optimized parameters of 105 m/min, 0.1 mm/tooth, and 1 mm. On the other hand, a neural network having a single hidden layer with 1–20 hidden neurons, 3 input neurons, and 1 output neuron was trained with both PSO and PSO–GSA algorithms. The hybrid BPNN–PSO–GSA model showed its superiority over the BPNN–PSO model in terms of the minimum mean square error (MSE) that was calculated during the testing stage. The best BPNN–PSO–GSA hybrid model was the 3–18–1 structure, which reached the best testing MSE of 3.8 × 10−11 against 2.42 × 10−5 of the 3–8–1 BPNN–PSO hybrid model.
The single axis linear displacement measurement system of CMM is composed of grating ruler, servo motor and linear motion mechanism. Although the measuring accuracy of grating ruler is high, the accuracy of servo motor and linear motion mechanism is low. Therefore, the complex structure limits the measurement accuracy of the linear displacement measurement system. This paper introduces a novel linear displacement measurement system named magnetic levitation ruler. According to the working principle of grating ruler and the characteristics of magnetic levitation technology, the magnetic circuit design and structural design of magnetic levitation ruler are completed in this paper. The mover core of the magnetic levitation ruler is in the stable working magnetic field provided by the stator yoke. The horizontal control coil wound on the mover core can obtain more stable ampere force to improve the control accuracy of the mover core displacement. Therefore, the mover core can be moved in step mode, and the length of each step is fixed. Each step is the minimum scale of the magnetic levitation ruler. Therefore, the mover core can implement displacement measurement while moving in a linear motion. This paper analyzes the working principle of levitation, horizontal motion, and displacement measurement of magnetic levitation ruler, and determines the structural materials and parameters of magnetic levitation ruler with the help of finite element analysis software. The simulation results show that the levitation force of the magnetic levitation ruler is proportional to the current passing through the levitation coils, and the thrust of the horizontal control coil is less disturbed by the magnetic field. Compared with the linear displacement measurement system with rotational servo motor or permanent magnet synchronous linear motor as the core, the magnetic levitation ruler has stable magnetic field, strong controllability, high integration, and is easier to achieve high-precision control.
Control engineering systems. Automatic machinery (General), Technology (General)
Besides direct interaction, human hands are also skilled at using tools to manipulate objects for typical life and work tasks. This paper proposes DeepClaw 2.0 as a low-cost, open-sourced data collection platform for learning human manipulation. We use an RGB-D camera to visually track the motion and deformation of a pair of soft finger networks on a modified kitchen tong operated by human teachers. These fingers can be easily integrated with robotic grippers to bridge the structural mismatch between humans and robots during learning. The deformation of soft finger networks, which reveals tactile information in contact-rich manipulation, is captured passively. We collected a comprehensive sample dataset involving five human demonstrators in ten manipulation tasks with five trials per task. As a low-cost, open-sourced platform, we also developed an intuitive interface that converts the raw sensor data into state-action data for imitation learning problems. For learning-by-demonstration problems, we further demonstrated our dataset’s potential by using real robotic hardware to collect joint actuation data or using a simulated environment when limited access to the hardware.
Mechanical engineering and machinery, Electronic computers. Computer science
This study proposes a novel isolated bidirectional multiport converter (MPC) based on a switched-capacitor converter and a half-bridge converter with an effective control scheme for photovoltaic (PV) powered and battery buffered systems. The proposed power electronics converter interface integrates the converters to which the ports are connected with a battery coupled common dc busbar and high frequency transformer (HFT). Thus, the three-port converter is formed without any need for an additional converter to regulate battery power flow. In addition, to transfer power from a low voltage PV energy unit to the battery and load, a single switch DC-DC converter with high voltage gain is proposed. The power flow between the ports is controlled by an effective multi-loop control scheme that is able to perform a smooth transition between the loops. In order to validate the viability and effectiveness of the proposed MPC, a 3 kW proof-of-concept model has been developed with a 3 kW PV and 220 V 12 Ah battery. The performance of the proposed converter has been analyzed for different case studies, including dynamic operating and loading conditions.
Early diagnosis of prostate cancer (PCa) is always a great challenge in clinical practice, especially in distinguishing benign prostatic hyperplasia (BPH) from early cancer, due to the high similarity in pathology from the prostate‐specific antigen (PSA) test and radiological detection. The conventional diagnostic methods are often less efficient in specificity and accuracy, leading to quite a few unnecessary biopsies. This work establishes a noninvasive diagnostic method for PCa by investigating urine samples using Raman spectroscopy and convolutional neural network (CNN) algorithm. The results of urine Raman spectra show the intensities of characteristic peaks for lipids, nucleic acids, and some amino acids are distinguishable between PCa and BPH, suggesting an abnormal metabolism caused by PCa, which can be detected by Raman spectroscopy. These data are then used to train an intelligent diagnostic model with CNN algorithm. The cross‐validation results show the mean diagnostic accuracy, sensitivity, and specificity for PCa are 74.95%, 77.32%, and 72.46%, respectively. This noninvasive diagnostic method is a promising method for the early diagnosis of PCa, and the idea of using urine Raman spectroscopy with deep learning techniques for diagnosing PCa provides a reference for the application of artificial intelligence in the field of clinical medicine research.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
This investigation means to optimize the properties of High-Performance-Concrete (HPC) containing fine aggregates from concrete and brick wastes for different recycled aggregates substitution rates, using mixture design modeling. To succeed this, the design of experiments (DOE) method was used. It is observed that slump, flexural and compressive strength of recycled concrete are significantly influenced by the content of natural sand (NS), recycled concrete aggregates (RCA) and recycled brick aggregates (RBA). The experimentally measured responses were successfully studied to develop a polynomial model, which represents slump, flexural strength and compressive strength at 7 day and 28 day of HPC. The significance and accuracy of the model was confirmed by statistical analysis and experimental verification. Under optimal conditions, the maximum desirability is 0.65, which can be obtained by using only recycled sands, i.e. RBA (9.5%) and RCA (90.5%), and no natural sand. The statistical results show that the proposed models are well correlated with the experimental data
Mechanical engineering and machinery, Structural engineering (General)
<p>In this paper, an analytical wake model with a double-Gaussian velocity
distribution is presented, improving on a similar formulation by <span class="cit" id="xref_text.1"><a href="#bib1.bibx14">Keane et al.</a> (<a href="#bib1.bibx14">2016</a>)</span>. The choice of a double-Gaussian shape function is motivated by the
behavior of the near-wake region that is observed in numerical simulations and
experimental measurements. The method is based on the conservation of
momentum principle, while stream-tube theory is used to determine the wake
expansion at the tube outlet. The model is calibrated and validated using
large eddy simulations replicating scaled wind turbine experiments. Results
show that the tuned double-Gaussian model is superior to a single-Gaussian
formulation in the near-wake region.</p>
Previous studies have proposed higher requirements for the transient characteristics of a DC transformer used in a flexible high-voltage direct current (HVDC) system to achieve faster sampling speed and meet wider bandwidth requirements of the control and protection signal, and to eventually suppress the large transient fault current. In this study, a transient characteristics verification method is proposed for transient characteristics verification of a DC transformer used in a flexible HVDC system based on resampling technology and LabVIEW measurement technology after analyzing the key technology for transient characteristics verification of a DC transformer. A laboratory experiment for the transient characteristics of a full-fiber electronic DC transformer is conducted, and experimental results show that such verification method can be employed for frequency response and step response verification of a DC transformer at 10% of the rated voltage and current, and can eventually improve the screening of a DC transformer. Keywords: DC transformer, Step response, Transient characteristic, Resampling technology, Verification method
Energy conservation, Energy industries. Energy policy. Fuel trade